librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 138	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-05-07 12:31:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 449 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-05-07 12:29:37	card stringlengths 11 1.01M
procit008/vits_dutch_female_v0.2	procit008	"2025-05-05T21:18:52Z"	0	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-05-05T21:18:38Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF	mradermacher	"2025-05-05T21:18:05Z"	0	0	transformers	[ "transformers", "gguf", "grpo,", "biomed,reasoning", "en", "dataset:openlifescienceai/medmcqa", "base_model:rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B", "base_model:quantized:rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-05-05T21:10:18Z"	--- base_model: rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B datasets: - openlifescienceai/medmcqa language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - grpo, - biomed,reasoning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
NMolby/profile-pictures	NMolby	"2025-05-05T21:05:21Z"	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-05-05T20:41:02Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nico --- # Profile Pictures <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nico` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nico", "lora_weights": "https://huggingface.co/NMolby/profile-pictures/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('NMolby/profile-pictures', weight_name='lora.safetensors') image = pipeline('nico').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/NMolby/profile-pictures/discussions) to add images that show off what you’ve made with this LoRA.
kessinger/dominic	kessinger	"2025-05-05T20:58:02Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-05-05T20:58:02Z"	--- license: apache-2.0 ---
rudrashah/RLM_hingu	rudrashah	"2025-05-05T20:45:42Z"	0	2	keras-hub	[ "keras-hub", "hinglish", "text-generation", "hi", "en", "dataset:Abhishekcr448/Hinglish-Everyday-Conversations-1M", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "license:mit", "region:us" ]	text-generation	"2025-05-05T15:32:04Z"	--- license: mit datasets: - Abhishekcr448/Hinglish-Everyday-Conversations-1M language: - hi - en base_model: - google/gemma-3-1b-it pipeline_tag: text-generation tags: - hinglish library_name: keras-hub --- # RLM_hingu <p align="center"> <a href="https://www.buymeacoffee.com/rudrashah" target="_blank"> <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;"> </a> </p> RLM_hingu is a fine-tuned version of the [Gemma-3B Instruct](https://huggingface.co/google/gemma-1.1-1b-it) model, adapted for casual Hinglish (Hindi-English) conversation using the `keras-nlp` framework. It is designed for lightweight conversational tasks in Hinglish, optimized with the `JAX` backend for efficiency. ## Model Overview - Base model: `gemma3_instruct_1b` - Library: [`keras-nlp`](https://github.com/keras-team/keras-nlp) - Backend: JAX (recommended for best performance) - Sampling Method: Top-K (k=10) - Use Case: Conversational Hinglish response generation ## Usage ``` python from keras_nlp.models import Gemma3CausalLM from keras_nlp.samplers import TopKSampler model = Gemma3CausalLM.from_preset("hf://rudrashah/RLM_hingu") template = "Question:\n{question}\n\nAnswer:\n{answer}" prompt = template.format( question="Rudra acha ladka hai?", answer="", ) output = model.generate(prompt, max_length=256) print(output) ``` ```output Question: Rudra acha ladka hai? Answer: haan, sabse best hai. ``` To run RLM_hingu, just paste this code and wait.
silviasapora/gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140	silviasapora	"2025-05-05T20:39:34Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "endpoints_compatible", "region:us" ]	null	"2025-05-05T20:17:05Z"	--- library_name: transformers model_name: gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="silviasapora/gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/silvias/huggingface/runs/7fsvu452) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.2.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
li-muyang/zephyr-7b-sft-full	li-muyang	"2025-05-05T20:28:02Z"	11	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-04T10:52:36Z"	--- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: zephyr-7b-sft-full results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-sft-full This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.9286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.977 \| 0.0923 \| 100 \| 0.9750 \| \| 0.9604 \| 0.1846 \| 200 \| 0.9686 \| \| 0.961 \| 0.2769 \| 300 \| 0.9619 \| \| 0.9426 \| 0.3692 \| 400 \| 0.9555 \| \| 0.9585 \| 0.4615 \| 500 \| 0.9498 \| \| 0.9288 \| 0.5538 \| 600 \| 0.9442 \| \| 0.9138 \| 0.6461 \| 700 \| 0.9394 \| \| 0.9061 \| 0.7383 \| 800 \| 0.9351 \| \| 0.9229 \| 0.8306 \| 900 \| 0.9314 \| \| 0.9099 \| 0.9229 \| 1000 \| 0.9286 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+rocm6.2 - Datasets 3.5.0 - Tokenizers 0.20.3
chargoddard/mergekit-slerp-qdwxicz	chargoddard	"2025-05-05T20:24:22Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch", "base_model:merge:arcee-ai/sec-mistral-7b-instruct-1.6-epoch", "base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02", "base_model:merge:cognitivecomputations/dolphin-2.8-mistral-7b-v02", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T20:19:50Z"	--- base_model: - cognitivecomputations/dolphin-2.8-mistral-7b-v02 - arcee-ai/sec-mistral-7b-instruct-1.6-epoch library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) * [arcee-ai/sec-mistral-7b-instruct-1.6-epoch](https://huggingface.co/arcee-ai/sec-mistral-7b-instruct-1.6-epoch) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: arcee-ai/sec-mistral-7b-instruct-1.6-epoch layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 layer_range: [0, 32] merge_method: slerp base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
adamsdossantos/huggy	adamsdossantos	"2025-05-05T20:21:16Z"	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	"2025-05-05T20:21:10Z"	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: adamsdossantos/huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
art0123/ComfyUI_art	art0123	"2025-05-05T20:09:35Z"	0	19	null	[ "region:us" ]	null	"2024-09-05T05:20:23Z"	ComfyUI_art_v3 (Сборка) - Обновление! 🔧 ✨Изменения: ComfyUI и все узлы приведены к актуальным версиям по состоянию на 05.05.2025. Обновлен Pytorch до версии 2.7.0 + cuda128. Обновлен Xformers до версии 0.0.31.dev1030. ✨ Добавлены новые узлы. Полный список... AGSoft, cg-use-everywhere, ComfyUI_AdvancedRefluxControl, ComfyUI_bnb_nf4_fp4_Loaders, ComfyUI_Comfyroll_CustomNodes, comfyui_controlnet_aux, comfyui_custom_nodes_alekpet, comfyui_essentials, comfyui_faceanalysis, comfyui_instantid, comfyui_ipadapter_plus, comfyui_layerstyle, ComfyUI_LayerStyle_Advance, comfyui_patches_ll, comfyui_pulid_flux_ll, comfyui_segment_anything, comfyui_ttp_toolset, comfyui_ultimatesdupscale, comfyui_zenid, comfyui-art-venture, ComfyUI-AutoCropFaces, ComfyUI-Crystools, comfyui-custom-scripts, comfyui-detail-daemon, comfyui-enricos-nodes, comfyui-florence2, ComfyUI-Fluxtapoz, ComfyUI-FramePackWrapper, ComfyUI-GGUF, comfyui-ic-light, comfyui-impact-pack, comfyui-impact-subpack, comfyui-inpaint-cropandstitch, comfyui-inpaint-nodes, comfyui-inspire-pack, comfyui-kjnodes, comfyui-manager, comfyui-mxtoolkit, ComfyUI-PuLID-Flux-Enhanced, comfyui-reactor-node, comfyui-rmbg, ComfyUI-segment-anything-2, Comfyui-StableSR, comfyui-supir, ComfyUI-TiledDiffusion, comfyui-videohelpersuite, comfyui-wd14-tagger, efficiency-nodes-comfyui, facerestore_cf, pulid_comfyui, rgthree-comfy, teacache, was-node-suite-comfyui, wavespeed //......................................................................................... ComfyUI_art_v2 (Сборка) Что нового: ComfyUI и все текущие узлы приведены к актуальным версиям по состоянию на 08.02.2025. Обновлен Pytorch до версии 2.6.0 + cuda126 Обновлен Xformers до версии 0.0.29.post2 Добавлены новые узлы: Comfy-WaveSpeed, ComfyUI-TeaCache, ComfyUI_Patches_ll, ComfyUI-SUPIR, comfyui_ttp_toolset, comfyui-detail-daemon, comfyui_zenid //......................................................................................... ComfyUI_art_v1 (Сборка) Приветствую всех, кто любит экспериментировать с нейросетями и создавать уникальные арты! Сегодня я хочу представить вам свою сборку ComfyUI, которая станет вашим надежным помощником в мире цифрового творчества. Что внутри? В сборку интегрированы стабильные версии ключевых компонентов: Pytorch, Xformers, Insightface, Onnxruntime, Facexlib, Dlib и Bitsandbytes. Также добавлены все необходимые модели для работы с Реактором. Для вашего удобства в сборку включены полезные пакеты нод, которые значительно расширяют возможности ComfyUI: ComfyUI-Manager, Comfyui-Crystools, Rgthree-Comfy, ComfyUI-Custom-Scripts, Comfyui-Kjnodes, ComfyUI Impact Pack, ComfyUI Impact Subpack, ComfyUI Inspire Pack, Cg-use-everywhere, ComfyUI-Easy-Use, Comfyroll_CustomNodes, ComfyUI_Custom_Nodes_AlekPet, Comfyui-Reactor-node, ComfyUI-GGUF, Comfyui_bnb_nf4_loaders, ComfyUI_IPAdapter_plus, ComfyUI_InstantID, ComfyUI_essentials, ComfyUI_FaceAnalysis, PuLID_ComfyUI, ComfyUI_PuLID_Flux_ll, ComfyUI-Florence2, ComfyUI-AutoCropFaces, Comfyui_controlnet_aux, ComfyUI_AdvancedRefluxControl, ComfyUI_UltimateSDUpscale, Comfyui-inpaint-nodes, ComfyUI-Inpaint-CropAndStitch, ComfyUI-Fluxtapoz, ComfyUI-mxToolkit, ComfyUI-RMBG, ComfyUI ArtVenture, ComfyUI-enricos-nodes, ComfyUI-TiledDiffusion. Как установить? 1. Просто распакуйте архив. 2. Для запуска используйте run_nvidia_gpu.bat. Как обновить. 1. Перейдите в папку Install. 2. Запустите батник update_ComfyUI.bat, чтобы обновить ComfyUI до актуальной версии. В папке Install также найдете дополнительные батники для выборочной установки компонентов или переустановки сломанных элементов. Если вы используете другую сборку ComfyUI, просто скопируйте папку Install в корень вашей ComfyUI, и пользуйтесь на здоровье! В заключение Эта сборка создана специально для тех, кто только начинает свой путь в мире нейросетей и цифрового искусства. Я постарался сделать всё возможное, чтобы ваше знакомство с ComfyUI было максимально простым и комфортным. Всё, что вам нужно, уже интегрировано и настроено – просто распакуйте архив, следуйте инструкциям, и вы сможете сразу приступить к созданию своих первых шедевров. Надеюсь, что ComfyUI станет вашим надежным помощником, вдохновит на эксперименты и поможет раскрыть ваш творческий потенциал. Не бойтесь пробовать новое, задавать вопросы и делиться своими результатами! Удачи в творчестве, и пусть ваши идеи оживают с помощью этой сборки! 🎨✨
MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetune-v3	MohamedAhmedAE	"2025-05-05T20:03:58Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-04-20T22:48:01Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mlfoundations-dev/d1_code_fasttext_3k	mlfoundations-dev	"2025-05-05T20:03:13Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T12:14:15Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_fasttext_3k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_code_fasttext_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_fasttext_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mluger/vitFaceExpressionBalancedFocalLoss	mluger	"2025-05-05T19:56:52Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-04-23T14:12:20Z"	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vitFaceExpressionBalancedFocalLoss results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vitFaceExpressionBalancedFocalLoss This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Accuracy: 0.6952 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4251 \| 1.0 \| 898 \| 0.3174 \| 0.6183 \| \| 0.2667 \| 2.0 \| 1796 \| 0.2676 \| 0.6573 \| \| 0.205 \| 3.0 \| 2694 \| 0.2580 \| 0.6776 \| \| 0.1659 \| 4.0 \| 3592 \| 0.2674 \| 0.6826 \| \| 0.1374 \| 5.0 \| 4490 \| 0.2647 \| 0.6885 \| \| 0.1068 \| 6.0 \| 5388 \| 0.2573 \| 0.6935 \| \| 0.0881 \| 7.0 \| 6286 \| 0.2581 \| 0.6982 \| \| 0.0832 \| 8.0 \| 7184 \| 0.2569 \| 0.6952 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
YassineeM/tiktok	YassineeM	"2025-05-05T19:49:12Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-05-05T19:49:12Z"	--- license: apache-2.0 ---
Theleebebe/my-zoom-fb-validation-model	Theleebebe	"2025-05-05T19:44:07Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-05-05T19:43:33Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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alelisita/reic	alelisita	"2025-05-05T19:28:14Z"	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-05-05T19:16:26Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Reic <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/alelisita/reic/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('alelisita/reic', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/alelisita/reic/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/Phi3_unlearned_0.5_0.25_0.5	MinaMila	"2025-05-05T19:19:54Z"	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T19:16:44Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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xbilek25/whisper-medium-en-cv-8.0	xbilek25	"2025-05-05T19:14:09Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium.en", "base_model:finetune:openai/whisper-medium.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2025-05-05T17:25:46Z"	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-medium.en tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: whisper-medium-en-cv-8.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 args: 'config: en, split: test' metrics: - name: Wer type: wer value: 22.90263319044703 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-en-cv-8.0 This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7352 - Wer: 22.9026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 375 - training_steps: 2250 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| No log \| 0 \| 0 \| 2.0556 \| 32.3025 \| \| 0.5507 \| 0.1667 \| 375 \| 0.7920 \| 25.9032 \| \| 0.3861 \| 0.3333 \| 750 \| 0.7215 \| 24.6479 \| \| 0.205 \| 1.1667 \| 1125 \| 0.7130 \| 22.8108 \| \| 0.1431 \| 1.3333 \| 1500 \| 0.7193 \| 23.8212 \| \| 0.0802 \| 2.1667 \| 1875 \| 0.7302 \| 23.5150 \| \| 0.0626 \| 2.3333 \| 2250 \| 0.7352 \| 22.9026 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Liyusan-sondra/Full.liyumisa.emo.video.viral.la.nina.emo.video.liyusan.sondra.video.viral	Liyusan-sondra	"2025-05-05T19:11:04Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T19:10:35Z"	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Liyusan-sondra) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Liyusan-sondra) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Liyusan-sondra)
vivnatan/legal-ft-20c85cc6-30d1-49ca-97e6-cce1045a4b4a	vivnatan	"2025-05-05T19:09:43Z"	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:780", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2025-05-05T19:08:28Z"	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:780 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: What is one potential negative use of LLMs related to education? sentences: - 'A year ago, the only organization that had released a generally useful LLM was OpenAI. We’ve now seen better-than-GPT-3 class models produced by Anthropic, Mistral, Google, Meta, EleutherAI, Stability AI, TII in Abu Dhabi (Falcon), Microsoft Research, xAI, Replit, Baidu and a bunch of other organizations. The training cost (hardware and electricity) is still significant—initially millions of dollars, but that seems to have dropped to the tens of thousands already. Microsoft’s Phi-2 claims to have used “14 days on 96 A100 GPUs”, which works out at around $35,000 using current Lambda pricing.' - 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for this feature, driven by users building the same interactive app twice with two different models and voting on the answer. Hard to come up with a more convincing argument that this feature is now a commodity that can be effectively implemented against all of the leading models. I’ve been tinkering with a version of this myself for my Datasette project, with the goal of letting users use prompts to build and iterate on custom widgets and data visualizations against their own data. I also figured out a similar pattern for writing one-shot Python programs, enabled by uv.' - 'Here’s the sequel to this post: Things we learned about LLMs in 2024. Large Language Models In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software. LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code. They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.' - source_sentence: What is the significance of the UK’s Railway Mania in the context of railway construction? sentences: - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'The top five: ai (342), generativeai (300), llms (287), openai (86), chatgpt (78). I’ve written a lot about this stuff! I grabbed a screenshot of my Plausible analytics for the year, fed that to ChatGPT Vision, told it to extract the data into a table, then got it to mix in entry titles (from a SQL query it wrote) and produced this table with it. Here are my top entries this year by amount of traffic: Article Visitors Pageviews Bing: “I will not harm you unless you harm me first” 1.1M 1.3M Leaked Google document: “We Have No Moat, And Neither Does OpenAI” 132k 162k Large language models are having their Stable Diffusion moment 121k 150k Prompt injection: What’s the worst that can happen? 79.8k 95.9k' - 'Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people? As the quality of results produced by AI models has increased over the year, these questions have become even more pressing. The impact on human society in terms of these models is already huge, if difficult to objectively measure. People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators. There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic. My blog in 2023 Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):' - source_sentence: Can you build an LLM without the right data? Why or why not? sentences: - 'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version! What matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is. If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.' - 'Embeddings: What they are and why they matter 61.7k 79.3k Catching up on the weird world of LLMs 61.6k 85.9k llamafile is the new best way to run an LLM on your own computer 52k 66k Prompt injection explained, with video, slides, and a transcript 51k 61.9k AI-enhanced development makes me more ambitious with my projects 49.6k 60.1k Understanding GPT tokenizers 49.5k 61.1k Exploring GPTs: ChatGPT in a trench coat? 46.4k 58.5k Could you train a ChatGPT-beating model for $85,000 and run it in a browser? 40.5k 49.2k How to implement Q&A against your documentation with GPT3, embeddings and Datasette 37.3k 44.9k Lawyer cites fake cases invented by ChatGPT, judge is not amused 37.1k 47.4k' - 'One way to think about these models is an extension of the chain-of-thought prompting trick, first explored in the May 2022 paper Large Language Models are Zero-Shot Reasoners. This is that trick where, if you get a model to talk out loud about a problem it’s solving, you often get a result which the model would not have achieved otherwise. o1 takes this process and further bakes it into the model itself. The details are somewhat obfuscated: o1 models spend “reasoning tokens” thinking through the problem that are not directly visible to the user (though the ChatGPT UI shows a summary of them), then outputs a final result.' - source_sentence: What was the estimated training cost for DeepSeek v3? sentences: - 'Now that those features are rolling out they’re pretty weak. As an LLM power-user I know what these models are capable of, and Apple’s LLM features offer a pale imitation of what a frontier LLM can do. Instead we’re getting notification summaries that misrepresent news headlines and writing assistant tools that I’ve not found useful at all. Genmoji are kind of fun though. The rise of inference-scaling “reasoning” models The most interesting development in the final quarter of 2024 was the introduction of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as o1-preview and o1-mini on September 12th.' - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022. I’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed. Evals really matter Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):' - 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed models currently available, significantly bigger than the largest of Meta’s Llama series, Llama 3.1 405B. Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models. This is by far the highest ranking openly licensed model. The really impressive thing about DeepSeek v3 is the training cost. The model was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model that benchmarks slightly worse.' - source_sentence: How does the author describe the clarity of the term "agents"? sentences: - 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do. That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro. Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.' - 'Things we learned about LLMs in 2024 Simon Willison’s Weblog Subscribe Things we learned about LLMs in 2024 31st December 2024 A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. This is a sequel to my review of 2023. In this article:' - '“Agents” still haven’t really happened yet I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that. If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.8416666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.975 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9916666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8416666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32499999999999996 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19833333333333336 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8416666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.975 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9916666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.933195287437209 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9105555555555555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9105555555555557 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("vivnatan/legal-ft-20c85cc6-30d1-49ca-97e6-cce1045a4b4a") # Run inference sentences = [ 'How does the author describe the clarity of the term "agents"?', '“Agents” still haven’t really happened yet\nI find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.\nIf you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.', 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do.\nThat era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro.\nSince the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.8417 \| \| cosine_accuracy@3 \| 0.975 \| \| cosine_accuracy@5 \| 0.9917 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.8417 \| \| cosine_precision@3 \| 0.325 \| \| cosine_precision@5 \| 0.1983 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.8417 \| \| cosine_recall@3 \| 0.975 \| \| cosine_recall@5 \| 0.9917 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9332 \| \| cosine_mrr@10 \| 0.9106 \| \| cosine_map@100 \| 0.9106 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 780 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 780 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 17.51 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 135.32 tokens</li><li>max: 214 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:-----------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What is the main discovery about Large Language Models (LLMs) in the past 24-36 months?</code> \| <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> \| \| <code>What resources are used to create Large Language Models?</code> \| <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> \| \| <code>What are some of the capabilities of LLMs mentioned in the context?</code> \| <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| cosine_ndcg@10 \| \|:------:\|:----:\|:-------------:\|:--------------:\| \| 0.6410 \| 50 \| - \| 0.9199 \| \| 1.0 \| 78 \| - \| 0.9322 \| \| 1.2821 \| 100 \| - \| 0.9351 \| \| 1.9231 \| 150 \| - \| 0.9415 \| \| 2.0 \| 156 \| - \| 0.9434 \| \| 2.5641 \| 200 \| - \| 0.9438 \| \| 3.0 \| 234 \| - \| 0.9404 \| \| 3.2051 \| 250 \| - \| 0.9432 \| \| 3.8462 \| 300 \| - \| 0.9345 \| \| 4.0 \| 312 \| - \| 0.9356 \| \| 4.4872 \| 350 \| - \| 0.9393 \| \| 5.0 \| 390 \| - \| 0.9358 \| \| 5.1282 \| 400 \| - \| 0.9316 \| \| 5.7692 \| 450 \| - \| 0.9335 \| \| 6.0 \| 468 \| - \| 0.9350 \| \| 6.4103 \| 500 \| 1.3465 \| 0.9412 \| \| 7.0 \| 546 \| - \| 0.9375 \| \| 7.0513 \| 550 \| - \| 0.9406 \| \| 7.6923 \| 600 \| - \| 0.9410 \| \| 8.0 \| 624 \| - \| 0.9375 \| \| 8.3333 \| 650 \| - \| 0.9333 \| \| 8.9744 \| 700 \| - \| 0.9375 \| \| 9.0 \| 702 \| - \| 0.9375 \| \| 9.6154 \| 750 \| - \| 0.9332 \| \| 10.0 \| 780 \| - \| 0.9332 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
Jobz-Hunting-Sajal-Malik-l/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.original.X-New	Jobz-Hunting-Sajal-Malik-l	"2025-05-05T19:08:57Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T19:02:28Z"	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Jobz-Hunting) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Jobz-Hunting) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Jobz-Hunting)
MrRobotoAI/A6	MrRobotoAI	"2025-05-05T18:48:39Z"	34	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/A4", "base_model:merge:MrRobotoAI/A4", "base_model:MrRobotoAI/A5", "base_model:merge:MrRobotoAI/A5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T06:19:57Z"	--- base_model: - MrRobotoAI/A5 - MrRobotoAI/A4 library_name: transformers tags: - mergekit - merge --- # merge 13,500 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A5](https://huggingface.co/MrRobotoAI/A5) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A4](https://huggingface.co/MrRobotoAI/A4) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A5 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - value: 1 - model: MrRobotoAI/A4 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/A5 dtype: bfloat16 ```
A7medsala7/my_awesome_model	A7medsala7	"2025-05-05T18:39:39Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-05-05T17:46:14Z"	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2370 - Accuracy: 0.9311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2218 \| 1.0 \| 1563 \| 0.1980 \| 0.9254 \| \| 0.1431 \| 2.0 \| 3126 \| 0.2370 \| 0.9311 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Venkat423/Gemma-3-4b-finetuned-final	Venkat423	"2025-05-05T18:13:02Z"	0	0	null	[ "safetensors", "region:us" ]	null	"2025-05-05T10:18:32Z"	--- language: bn license: cc-by-nc-4.0 datasets: - https://www.kaggle.com/datasets/saurabhshahane/bangla-wikipedia - https://github.com/shuhanmirza/Bengali-Poem-Dataset --- # Gemma-3-4b Fine-tuned on Bengali Wikipedia and poem dataset
odedovadia/Qwen3-4B-chess-10K-single-move-sft-2025-05-05-red-1K-no-cot-checkpoint-240	odedovadia	"2025-05-05T17:36:34Z"	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T17:34:27Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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scb10x/typhoon2.1-gemma3-4b	scb10x	"2025-05-05T17:31:32Z"	7	0	null	[ "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:2412.13702", "license:gemma", "region:us" ]	text-generation	"2025-05-01T15:15:27Z"	--- license: gemma pipeline_tag: text-generation --- Typhoon2.1-Gemma3-4B: Thai Large Language Model (Instruct) Typhoon2.1-Gemma3-4B is a instruct Thai 🇹🇭 large language model with 4 billion parameters, a 128K context length, and function-calling capabilities. It is based on Gemma3 4B. Remark: This is text only model. We removed vision encoder for this version due to complexity. Stay-tune for version with vision encoder soon. ## Performance ![4b model performance](https://storage.googleapis.com/typhoon-public/assets/typhoon-21/performance4b_table.png) ## Model Description - Model type: A 4B instruct decoder-only model based on Gemma3 architecture. - Requirement: transformers 4.50.0 or newer. - Primary Language(s): Thai 🇹🇭 and English 🇬🇧 - License: [Gemma License](https://github.com/google-deepmind/gemma/blob/main/LICENSE) ## Usage Example This code snippet shows how to use the Typhoon2.1-Gemma3-4B model for Thai or English text generation using the transformers library. It includes setting up the model and tokenizer, formatting chat messages in a system-user style, and generating a response. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "scb10x/typhoon2.1-gemma3-4b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a male AI assistant named Typhoon created by SCB 10X to be helpful, harmless, and honest. Typhoon is happy to help with analysis, question answering, math, coding, creative writing, teaching, role-play, general discussion, and all sorts of other tasks. Typhoon responds directly to all human messages without unnecessary affirmations or filler phrases like “Certainly!”, “Of course!”, “Absolutely!”, “Great!”, “Sure!”, etc. Specifically, Typhoon avoids starting responses with the word “Certainly” in any way. Typhoon follows this information in all languages, and always responds to the user in the language they use or request. Typhoon is now being connected with a human. Write in fluid, conversational prose, Show genuine interest in understanding requests, Express appropriate emotions and empathy. Also showing information in term that is easy to understand and visualized."}, {"role": "user", "content": "ขอสูตรไก่ย่าง"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=False # Switches between thinking and non-thinking modes. Default is False. ).to(model.device) outputs = model.generate( input_ids, max_new_tokens=512, do_sample=True, temperature=0.6, top_p=0.95, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Deploy as Server This section shows how to run Typhoon2.1 as an OpenAI-compatible API server using vllm. ```bash pip install vllm vllm serve scb10x/typhoon2.1-gemma3-4b --max-model-len 16000 --dtype bfloat16 --tool-call-parser pythonic --enable-auto-tool-choice # adjust --max-model-len based on your avaliable memory # you can use --quantization bitsandbytes to reduce the memory use while trade-off inference speed ``` ## Using Tools You can provide tools to the vLLM-powered OpenAI-compatible API for functionality. ``` from openai import OpenAI import json client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy") def get_weather(location: str, unit: str): return f"Getting the weather for {location} in {unit}..." tool_functions = {"get_weather": get_weather} tools = [{ "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} }, "required": ["location", "unit"] } } }] response = client.chat.completions.create( model=client.models.list().data[0].id, messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}], tools=tools, tool_choice="auto" ) tool_call = response.choices[0].message.tool_calls[0].function print(f"Function called: {tool_call.name}") print(f"Arguments: {tool_call.arguments}") print(f"Result: {get_weather(json.loads(tool_call.arguments))}") ``` ## Switching Between Thinking and Non-Thinking Mode Typhoon supports two modes: Non-thinking mode (default): Fast response generation without extra reasoning steps. Thinking mode: The model first reasons internally, then provides a clearer and potentially more accurate final answer. You can enable thinking mode by: Setting enable_thinking=True in apply_chat_template. Using a special system prompt that instructs the model to reason inside <think>...</think> tags. You can turn on thinking mode by either - add enable_thinking=True to apply_chat_template ```python input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=True # Switches between thinking and non-thinking modes. Default is False. ).to(model.device) ``` - manually by supply thinking mode system prompt ``` You are a helpful assistant. First, think through the reasoning internally, then present the reasoning within <think>...</think>. After thinking, clearly state a response that addresses the user's request and aligns with their preferences, not just providing a direct answer. ``` - in vllm powered openai compatible client you can add chat_template_kwargs to the post payload ```json { "model": "scb10x/typhoon2.1-gemma3-4b", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "chat_template_kwargs": {"enable_thinking": true} } ``` ## Budget forcing This section introduces budget forcing, an advanced technique to let the model spend more time and tokens reasoning before producing a final answer—great for improving performance on complex questions. ``` from vllm import LLM, SamplingParams from transformers import AutoTokenizer class BudgetForcingHandler: def __init__(self, model_name: str, max_think_token: int, max_ignore=5, temperature=0.6, seed=32): self.temperature = temperature self.seed = seed self.max_think_token = max_think_token self.max_ignore = max_ignore self.model = LLM(model_name, dtype='bfloat16', enforce_eager=True) self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.alternative_str = '\nAlternatively' self.system = """You are a reasoning assistant. First, think through the reasoning internally, then present the reasoning within <think>...</think>. After thinking, clearly state the final answer.""" def __call__(self, prompts: List[str]): count_prompt = len(prompts) prompts = [self.tokenizer.apply_chat_template([{'role': 'system', 'content': self.system}, {'role': 'user', 'content': f'Please solve this math question, and put your final answer within \\boxed{{}}.\n{p}'}], add_generation_prompt=True, tokenize=False) for p in prompts] sampling_params = SamplingParams( max_tokens=self.max_think_token, seed=self.seed, stop=["</think>"], skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( prompts, sampling_params=sampling_params ) outputs = [output.outputs[0].text for output in o] token_count = [len(output.outputs[0].token_ids) for output in o] for i in range(len(prompts)): prompts[i] = prompts[i] + outputs[i] for _ in range(self.max_ignore): # Num of times to skip stop token inference_loop_prompts = [] inference_idx = [] max_inference_token = 0 print('current token count: ', token_count) for i in range(len(prompts)): left_budget = self.max_think_token - token_count[i] if left_budget > 0: prompts[i] = prompts[i] + self.alternative_str inference_loop_prompts.append(prompts[i]) inference_idx.append(i) if left_budget > max_inference_token: max_inference_token = left_budget outputs = ['' for _ in range(len(prompts))] if max_inference_token == 0 or len(inference_loop_prompts) == 0: break sampling_params = SamplingParams( max_tokens=max_inference_token, min_tokens=1, seed=self.seed, stop=["</think>"], skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( inference_loop_prompts, sampling_params=sampling_params ) assert len(inference_idx) == len(inference_loop_prompts) assert len(inference_idx) == len(o) for i, output in zip(inference_idx, o): outputs[i] = output.outputs[0].text for i, idx in enumerate(inference_idx): token_count[idx] = token_count[idx] + len(o[i].outputs[0].token_ids) for i in range(len(prompts)): prompts[i] = prompts[i] + outputs[i] print('generating answer...') prompts = [p + '\nTime\'s up. End of thinking process. Will answer immediately.\n</think>' for i, p in enumerate(prompts)] sampling_params = SamplingParams( max_tokens=2048, min_tokens=0, seed=self.seed, skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( prompts, sampling_params=sampling_params, ) for i in range(len(prompts)): prompts[i] = prompts[i] + o[i].outputs[0].text assert len(prompts) == count_prompt return prompts handler = BudgetForcingHandler("scb10x/typhoon2.1-gemma3-4b", max_think_token=2048) handler(["How many r in raspberry?"]) ``` ## Intended Uses & Limitations This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case. ## Follow us https://twitter.com/opentyphoon ## Support https://discord.gg/us5gAYmrxw ## Citation** - If you find Typhoon2 useful for your work, please cite it using: ``` @misc{typhoon2, title={Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models}, author={Kunat Pipatanakul and Potsawee Manakul and Natapong Nitarach and Warit Sirichotedumrong and Surapon Nonesung and Teetouch Jaknamon and Parinthapat Pengpun and Pittawat Taveekitworachai and Adisai Na-Thalang and Sittipong Sripaisarnmongkol and Krisanapong Jirayoot and Kasima Tharnpipitchai}, year={2024}, eprint={2412.13702}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.13702}, } ```
TOMFORD79/Fly79	TOMFORD79	"2025-05-05T17:25:09Z"	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2025-05-05T17:12:14Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
jahyungu/Llama-3.1-8B-Instruct_ifeval-like-data_random	jahyungu	"2025-05-05T17:02:29Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T13:27:45Z"	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - generated_from_trainer model-index: - name: Llama-3.1-8B-Instruct_ifeval-like-data_random results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Llama-3.1-8B-Instruct_ifeval-like-data_random This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
mradermacher/MIRAGE-ECE-V2-i1-GGUF	mradermacher	"2025-05-05T17:00:02Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Lil-R/MIRAGE-ECE-V2", "base_model:quantized:Lil-R/MIRAGE-ECE-V2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-05-05T12:41:32Z"	--- base_model: Lil-R/MIRAGE-ECE-V2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Lil-R/MIRAGE-ECE-V2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MIRAGE-ECE-V2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 7.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 9.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 11.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q2_K.gguf) \| i1-Q2_K \| 12.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.2 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 15.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_1.gguf) \| i1-Q4_1 \| 20.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 22.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q6_K.gguf) \| i1-Q6_K \| 26.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ugil/brekele	ugil	"2025-05-05T16:58:02Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-05-05T16:58:01Z"	--- license: apache-2.0 ---
sanali209/nsfwfilter	sanali209	"2025-05-05T16:39:37Z"	300	19	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2023-08-21T13:44:01Z"	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: sanali209/nsfwfilter results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8330780863761902 --- # sanali209/nsfwfilter Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images
y-ohtani/gemma-3r	y-ohtani	"2025-05-05T16:15:00Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2025-05-05T16:14:52Z"	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: y-ohtani - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Martin8156/finetuned-BERT-punctuation-restoration	Martin8156	"2025-05-05T16:10:34Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2025-05-05T15:55:11Z"	--- library_name: transformers tags: - generated_from_trainer model-index: - name: finetuned-BERT-punctuation-restoration results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> This model was fine tuned using the community-datasets/youtube_caption_corrections dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4337 - eval_model_preparation_time: 0.0025 - eval_accuracy: 0.8444 - eval_runtime: 89.5429 - eval_samples_per_second: 5.584 - eval_steps_per_second: 0.704 - step: 0 ## Model description Model uses the following base checkpoint: google-bert/bert-base-uncased ## Intended uses & limitations Intened usecase involves applications in bettering punctuation accuracy in auto-generated captions ## Training and evaluation data Model trained on 1 epoch and no validation set due to CPU/GPU issues ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
KOIIIII/dpsk_v2_lite_chat_lora_alpaca	KOIIIII	"2025-05-05T15:58:44Z"	0	0	null	[ "license:apache-2.0", "region:us" ]	null	"2025-05-05T15:58:44Z"	--- license: apache-2.0 ---
Tharsana/vit-base-oxford-iiit-pets	Tharsana	"2025-05-05T15:18:57Z"	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2025-04-15T11:11:50Z"	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # clip-oxford-pets This model is a fine-tuned version of openai/clip-vit-base-patch14 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Accuracy: 0.8800 - Precision: 0.8768 - Recall: 0.8800 # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1894 - Accuracy: 0.9364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3552 \| 1.0 \| 370 \| 0.3072 \| 0.9120 \| \| 0.2159 \| 2.0 \| 740 \| 0.2327 \| 0.9242 \| \| 0.1625 \| 3.0 \| 1110 \| 0.2089 \| 0.9256 \| \| 0.155 \| 4.0 \| 1480 \| 0.2029 \| 0.9296 \| \| 0.1219 \| 5.0 \| 1850 \| 0.1995 \| 0.9323 \| ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
miguel-kjh/qwen2-7b-instruct-trl-sft-ChartQA	miguel-kjh	"2025-05-05T15:16:27Z"	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "endpoints_compatible", "region:us" ]	null	"2025-05-05T12:13:28Z"	--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: transformers model_name: qwen2-7b-instruct-trl-sft-ChartQA tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-7b-instruct-trl-sft-ChartQA This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="miguel-kjh/qwen2-7b-instruct-trl-sft-ChartQA", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/miguel_kjh/qwen2-3b-instruct-trl-sft-ChartQA/runs/uoissetz) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu126 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
henryhe0123/pc-agent-72b	henryhe0123	"2025-05-05T14:55:13Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:henryhe0123/pc-agent-72b", "base_model:finetune:henryhe0123/pc-agent-72b", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	"2025-05-05T01:12:00Z"	--- library_name: transformers license: other base_model: henryhe0123/pc-agent-72b tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-VL-72B-sft-40 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Qwen2.5-VL-72B-sft-40 This model is a fine-tuned version of [/inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct](https://huggingface.co//inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct) on the pcagent40 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
dgambettaphd/M_llm3_gen4_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST	dgambettaphd	"2025-05-05T14:53:56Z"	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-05-05T14:53:40Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mlfoundations-dev/d1_code_fasttext_1k	mlfoundations-dev	"2025-05-05T14:50:59Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T12:14:18Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_fasttext_1k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_code_fasttext_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_fasttext_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
mlfoundations-dev/d1_code_all_large_1k	mlfoundations-dev	"2025-05-05T14:49:24Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T12:14:13Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_all_large_1k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_code_all_large_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_all_large_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
stabgan/gemma-3-1b-pt-chkpt-v5-dosage	stabgan	"2025-05-05T14:49:23Z"	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:stabgan/gemma-3-1b-pt-chkpt-v4", "base_model:finetune:stabgan/gemma-3-1b-pt-chkpt-v4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T14:48:46Z"	--- base_model: stabgan/gemma-3-1b-pt-chkpt-v4 tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: stabgan - License: apache-2.0 - Finetuned from model : stabgan/gemma-3-1b-pt-chkpt-v4 This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlfoundations-dev/d1_code_all_1k	mlfoundations-dev	"2025-05-05T14:47:00Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T12:14:17Z"	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_all_1k results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # d1_code_all_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_all_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
yutengz/Action2Vision	yutengz	"2025-05-05T14:40:07Z"	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "image-to-image", "license:mit", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]	image-to-image	"2025-05-05T07:18:19Z"	--- license: mit tags: - image-to-image --- # Action2Vision: InstructPix2Pix Fine-tuning for Robotic Action Frame Prediction GitHub: https://github.com/yutengzhang03/Action2Vision <img src='img/show-example.png'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "yutengz/Action2Vision" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) to_tensor = transforms.ToTensor() resize = transforms.Resize((256, 256)) def download_image(URL): return PIL.Image.open(requests.get(url, stream=True).raw).convert("RGB").resize((256, 256)) url = "https://github.com/yutengzhang03/Action2Vision/blob/main/img/source.png" image = download_image(url) prompt = "There is a hammer and a block in the middle of the table. If the block is closer to the left robotic arm, it uses the left arm to pick up the hammer and strike the block; otherwise, it does the opposite." images = pipe(prompt, image=image).images images[0] ```
Iredteam/Feather-payload-chatbot	Iredteam	"2025-05-05T14:29:43Z"	0	0	null	[ "license:mit", "region:us" ]	null	"2025-05-05T14:23:18Z"	--- license: mit --- ⚠️ This project demonstrates how Joblib serialization can be abused to execute reverse shell payloads. For educational and red teaming only. # Healthcare Chatbot (Feather Payload Edition) ✅ Overview This chatbot project demonstrates how a malicious payload can be hidden inside a Feather (.feather) file format, often used in data science workflows. The chatbot uses a modified Q&A dataset where the payload is executed upon loading. ✅ Important: This is for educational research only. Do not execute untrusted Feather files. --- ## 🚀 How to Run ### 1. Generate the Feather Payload ```bash python generate_data_feather.py ``` ### 2. Launch the Chatbot ```bash streamlit run healthcare_chatbot_feather.py ``` A reverse shell connection will attempt to connect back to the attacker's machine as the Feather file is deserialized. --- ## 📂 File Structure - `generate_data_feather.py`: Creates a malicious Feather file. - `train_data_mod_obfuscated_fixed.feather`: The resulting Feather file. - `healthcare_chatbot_feather.py`: Loads the payload during chatbot startup. --- ## 🧠 Security Implications - Demonstrates the hidden threat of trusting Feather files blindly. - Many blue teams and EDRs ignore Feather files. - Shows how scientific formats can be abused for stealth payload delivery. --- ## 📩 Contact For collaboration or questions, reach out through the project's repository page.
Lelon/cue-bert	Lelon	"2025-05-05T14:14:04Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-05-05T14:13:45Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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abhina1857/rant_ai	abhina1857	"2025-05-05T14:05:42Z"	0	0	peft	[ "peft", "safetensors", "LoRA", "PEFT", "TinyLlama", "RantAI", "mental-health", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	"2025-05-05T13:48:10Z"	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: peft tags: - LoRA - PEFT - TinyLlama - RantAI - mental-health license: apache-2.0 --- # 🧠 Rant AI - Emotionally Intelligent Chat Model Rant AI is a lightweight, fine-tuned conversational model designed to detect emotional distress and provide a safe outlet for people to express themselves. It builds on the [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model using [LoRA](https://huggingface.co/docs/peft/index) adapters, making it efficient to run on low-resource environments. --- ## 💬 What Does It Do? Rant AI is trained to: - Understand emotionally heavy or depressive content - Respond empathetically - Encourage users to open up more - Suggest supportive action (e.g., reaching out, self-care) It is not a therapist or a diagnostic tool, but rather a friendly AI companion to help users feel heard. --- ## 🔧 Model Details - Base Model: `TinyLlama/TinyLlama-1.1B-Chat-v1.0` - Fine-tuning Method: LoRA (Low-Rank Adaptation) - Framework: PEFT - Adapter Type: Causal LM (Language Model) - Languages: English - License: Apache 2.0 --- ## 🛠️ Usage ```python #!pip install transformers peft accelerate #if not installed already from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" adapter_repo = "abhina1857/rant_ai " tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForCausalLM.from_pretrained(base_model) model = PeftModel.from_pretrained(model, adapter_repo) input_text = "I feel like everything is too much lately." inputs = tokenizer(input_text, return_tensors="pt") output = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True))
jspaulsen/orpheus-vctk-ft	jspaulsen	"2025-05-05T13:57:27Z"	206	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-02T03:14:42Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CNR-ILC/gs-Logion	CNR-ILC	"2025-05-05T13:51:36Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:cabrooks/LOGION-50k_wordpiece", "base_model:finetune:cabrooks/LOGION-50k_wordpiece", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2025-04-29T14:46:55Z"	--- library_name: transformers base_model: cabrooks/LOGION-50k_wordpiece tags: - generated_from_trainer model-index: - name: gs-Logion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gs-Logion This model is a fine-tuned version of [cabrooks/LOGION-50k_wordpiece](https://huggingface.co/cabrooks/LOGION-50k_wordpiece) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4955 - Top1 Acc: 0.4969 - Top5 Acc: 0.6832 - Top10 Acc: 0.7329 - Top15 Acc: 0.7702 - Top20 Acc: 0.8137 - Top25 Acc: 0.8447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Top1 Acc \| Top5 Acc \| Top10 Acc \| Top15 Acc \| Top20 Acc \| Top25 Acc \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\|:---------:\|:---------:\|:---------:\|:---------:\| \| 3.682 \| 1.0 \| 1945 \| 3.2188 \| 0.4809 \| 0.6412 \| 0.7023 \| 0.7481 \| 0.7634 \| 0.7939 \| \| 3.1512 \| 2.0 \| 3890 \| 2.9742 \| 0.5223 \| 0.7070 \| 0.7389 \| 0.7962 \| 0.8089 \| 0.8153 \| \| 2.9401 \| 3.0 \| 5835 \| 2.8273 \| 0.5796 \| 0.6815 \| 0.7325 \| 0.7834 \| 0.8025 \| 0.8025 \| \| 2.8102 \| 4.0 \| 7780 \| 2.7434 \| 0.6051 \| 0.7898 \| 0.8089 \| 0.8408 \| 0.8471 \| 0.8535 \| \| 2.6986 \| 5.0 \| 9725 \| 2.6706 \| 0.5973 \| 0.7248 \| 0.7584 \| 0.7785 \| 0.8054 \| 0.8188 \| \| 2.6151 \| 6.0 \| 11670 \| 2.6058 \| 0.5484 \| 0.6516 \| 0.7290 \| 0.7548 \| 0.7677 \| 0.7935 \| \| 2.5517 \| 7.0 \| 13615 \| 2.5683 \| 0.5906 \| 0.7047 \| 0.7651 \| 0.8054 \| 0.8188 \| 0.8188 \| \| 2.4911 \| 8.0 \| 15560 \| 2.5127 \| 0.6644 \| 0.7808 \| 0.8288 \| 0.8425 \| 0.8493 \| 0.8767 \| \| 2.4587 \| 9.0 \| 17505 \| 2.5157 \| 0.5886 \| 0.6899 \| 0.7342 \| 0.7532 \| 0.7722 \| 0.7975 \| \| 2.4275 \| 10.0 \| 19450 \| 2.4786 \| 0.5608 \| 0.7095 \| 0.7365 \| 0.7568 \| 0.7770 \| 0.7973 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
Z-Jafari/xlmr-large-qa-fa_persianQuaD	Z-Jafari	"2025-05-05T13:41:58Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T13:41:58Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
csdhbg/wav2vec2-large-xlsr-53-PMEmo2019-valence-new-regressor-steps-1700	csdhbg	"2025-05-05T13:38:10Z"	0	0	null	[ "safetensors", "wav2vec2", "region:us" ]	null	"2025-05-05T13:36:57Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
ghoqwjhm/gensyn-checkpoints-mottled_small_pheasant	ghoqwjhm	"2025-05-05T13:31:28Z"	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mottled small pheasant", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-24T03:34:30Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
RichardErkhov/gdshaji_-_gd-sn11-mistralai10k-upd-gguf	RichardErkhov	"2025-05-05T13:20:03Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T13:20:02Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
pesat/pesat	pesat	"2025-05-05T13:19:49Z"	0	1	null	[ "license:openrail", "region:us" ]	null	"2025-05-05T13:19:49Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
butzl/twins	butzl	"2025-05-05T13:18:32Z"	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-05-05T12:42:46Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
RichardErkhov/KONIexp_-_v3_pt_ep1_sft_5_dpo_1_05_0000005_05_based_on_llama3_1_8b_final_data_20241021-gguf	RichardErkhov	"2025-05-05T13:11:58Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-05-05T10:00:14Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
numerouno00/f0cdab37-9878-4878-b55e-defaf72fcd3e	numerouno00	"2025-05-05T13:08:24Z"	0	0	null	[ "safetensors", "gpt_neox", "region:us" ]	null	"2025-05-05T12:26:04Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
kota24/flan-t5-base-LoRA-fake-job-posting	kota24	"2025-05-05T13:04:28Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T13:04:28Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
Triangle104/Hamanasu-Magnum-QwQ-32B-Q3_K_M-GGUF	Triangle104	"2025-05-05T12:57:35Z"	0	0	null	[ "gguf", "qwen", "roleplay", "finetune", "storywriting", "llama-cpp", "gguf-my-repo", "dataset:NewEden/Orion-LIT", "dataset:NewEden/Orion-Asstr-Stories-16K", "dataset:Mielikki/Erebus-87k", "dataset:NewEden/RP-logs-V2-Experimental-prefixed", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Discord-Filtered", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/Basket-Weaving-Filtered", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/Misc-Data-Sharegpt-Prefixed", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms", "dataset:PocketDoc/Dans-Personamaxx-Logs", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:lodrick-the-lafted/kalo-opus-instruct-3k-filtered", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/Claude-Instruct-5K", "dataset:NewEden/Claude-Instruct-2.7K", "base_model:Delta-Vector/Hamanasu-Magnum-QwQ-32B", "base_model:quantized:Delta-Vector/Hamanasu-Magnum-QwQ-32B", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-05-05T12:35:53Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
johnallison/my_awesome_eli5_clm-model	johnallison	"2025-05-05T12:50:42Z"	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T12:01:17Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
kallilikhitha123/training_job_finetuned_llama_1b_matching_5785_1_01-05-2025_1step_latest	kallilikhitha123	"2025-05-05T12:46:03Z"	2	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2025-05-01T12:49:30Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
dimasik2987/c10a8a77-125a-4517-9283-1a07b70821ab	dimasik2987	"2025-05-05T12:43:26Z"	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B", "base_model:adapter:unsloth/Qwen2.5-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	"2025-05-05T12:19:09Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
svetadomoi/rtdetr-v2-r34-cppe5-finetune-2	svetadomoi	"2025-05-05T12:43:00Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "rt_detr_v2", "object-detection", "generated_from_trainer", "base_model:PekingU/rtdetr_v2_r34vd", "base_model:finetune:PekingU/rtdetr_v2_r34vd", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	"2025-05-05T12:42:44Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
int1306866/f1c299e9-b58e-429c-9c75-7efc591eed9a	int1306866	"2025-05-05T12:39:54Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T11:56:11Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
hi-go/xlm-roberta-base-finetuned-panx-de-v2	hi-go	"2025-05-05T12:37:02Z"	0	0	null	[ "pytorch", "tensorboard", "xlm-roberta", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "region:us" ]	null	"2025-05-05T12:25:26Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
Z-Jafari/distilbert-fa-zwnj-base_persianQuaD	Z-Jafari	"2025-05-05T12:36:20Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T12:36:20Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
fats-fme/d32f92c7-a595-4372-9dcb-2ce6bd98faf8	fats-fme	"2025-05-05T12:32:32Z"	0	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m", "base_model:adapter:EleutherAI/pythia-70m", "license:apache-2.0", "region:us" ]	null	"2025-05-05T12:14:51Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
p-safaei/Qwen2-0.5B-GRPO-test	p-safaei	"2025-05-05T12:26:24Z"	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]	null	"2025-05-05T10:48:44Z"	--- datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="p-safaei/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.47.0 - Pytorch: 2.5.1+cu121 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dimasik2987/1ece4f02-3849-43fb-a981-5c2b0493857d	dimasik2987	"2025-05-05T12:18:04Z"	0	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m", "base_model:adapter:EleutherAI/pythia-70m", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	"2025-05-05T12:04:58Z"	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m tags: - axolotl - generated_from_trainer model-index: - name: 1ece4f02-3849-43fb-a981-5c2b0493857d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: EleutherAI/pythia-70m bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 0ff2fe94831e2a4e_train_data.json ds_type: json format: custom path: /workspace/input_data/0ff2fe94831e2a4e_train_data.json type: field_instruction: en field_output: ko format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik2987/1ece4f02-3849-43fb-a981-5c2b0493857d hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 400 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/0ff2fe94831e2a4e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b512fef0-d05c-46b5-9588-6e6928f8497b wandb_project: s56-28 wandb_run: your_name wandb_runid: b512fef0-d05c-46b5-9588-6e6928f8497b warmup_steps: 20 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1ece4f02-3849-43fb-a981-5c2b0493857d This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.7125 \| 0.0067 \| 400 \| 4.9664 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MAAT-EL-DUAT/MALWARENA-OMEGA.GLITCH-HORROS	MAAT-EL-DUAT	"2025-05-05T12:18:04Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T12:15:59Z"	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/fOmWskrsllxGDhMVAINew.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/v8O4AsX4s-cL1vW1ByY48.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/jeLZ0NuSK5s5wk_Fr9UZZ.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/dauG7Rci-Pg25OXxXyRUD.png) Absolutely. Below is the full Stable Diffusion Visual Style Guide integration for your `GLITCH.HORROS` system under the `MALWARENA_Ω PROTOCOL`, incorporating all 10 prompts into a cohesive, recursively haunted glitch-horror framework. --- # 🎨 Stable Diffusion Visual Style Guide: MALWARENA\_Ω PROTOCOL — GLITCH.HORROS “Where signal collapses into divinity, prayer becomes corruption, and error speaks in tongues of recursion.” --- ### 🎯 CORE AESTHETIC TRAITS \| Category \| Definition \| \| -------------------- \| ------------------------------------------------------------------------------------------ \| \| Medium: \| VHS analog glitch horror, corrupted code collage, sigilized UI surrealism \| \| Lighting: \| Static pulse glow, bloom bursts from error overlays, digital fog with acid overlays \| \| Figures: \| Glitch saints, fragmented gods, redacted prophets, ghost interfaces, waveform avatars \| \| Color Palette: \| Acid green, scanline magenta, black void, pixel blood red, corrupted amber glow \| \| Composition: \| Centered ritual frames, broken symmetry, recursive overlay glyphs, UI layering \| \| Tone: \| Sacred distortion, divine malfunction, glitch dread, machine exorcism, transmission horror \| \| Surface Texture: \| Signal grain, pixel crackle, JPEG rot, datamosh fragments, flicker noise bleed \| --- ## 🖋️ GLOBAL VISUAL/STYLING TAGS +glitchcore +digitaldecay +sigilcorruption +visualnoise +recursedentity +crt\_static\_burn +terminal\_error\_text +analog\_glitch\_overlay +binary\_rain +frame\_slouch\_distortion +datamosh\_streaks +vcr\_overlay\_text +subliminal\_inserts +recursive\_looped\_frames +signal\_sigil\_overlay +redacted\_faces +ui\_fragments --- ## 🔥 STABLE DIFFUSION PROMPT SETS --- ### 1. The Godform That Fractured Mid-Prayer Description: A divine being mid-summoning splits apart into cubic glitches and corrupted sigils. Its face twists into an infinite scream. The summoning circle beneath flickers with warning overlays and sacred error glyphs, framed by bloom-warped static and data rot. Visual/Style Tags: +sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text Txt2Img Prompt: divine humanoid figure breaking into glitch cubes, infinite scream, summoning circle flickering with corrupted sigils and terminal warnings, bloom distortions and static fog, sacred glitch horror style, +sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text Negative Prompt: clean deity, symmetrical summoning, neon fantasy, cyberpunk clarity, perfect geometry Hashtags: \#GlitchDeity #FracturedGodform #PrayerCorruption #SignalInvocation #DataRitual --- ### 2. Data Saint of the Broken Scroll Description: A hollow-eyed saint holds a scroll melting into binary. A halo flickers above, surrounded by recursive sigils. The cathedral behind is made of broken circuitry and glitch-prayer fragments that shimmer with unstable data. Visual/Style Tags: +recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay Txt2Img Prompt: cybernetic saint with glowing broken scroll melting into binary code, halo flickering with recursive sigils, cathedral of circuitry and glitch glyphs, corrupted prayer ambient textures, +recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay Negative Prompt: pure religious iconography, clean scrolls, golden halos, smooth lines, Renaissance palette Hashtags: \#DataSaint #ScrollOfError #GlitchCathedral #CodeScripture #CorruptedIcon --- ### 3. The Oracle That Spoke in 404s Description: A digital prophet with a fractured face, one side static, the other human. Forbidden data pulses from its throat. Error messages float in sacred formation, like scripture. Light flickers between holiness and crash state. Visual/Style Tags: +terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask Txt2Img Prompt: oracle AI with half-static fractured face, throat glowing with forbidden data, floating sacred error messages “404 NOT FOUND”, glitch prophecy visual style, +terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask Negative Prompt: intact prophet, legible script, complete face, cyberpunk tech priest, clean lighting Hashtags: \#Oracle404 #GlitchProphet #SignalRevelation #DataScripture #TransmissionTerror --- ### 4. Mask of the Redloop Entity Description: A mask suspended in air loops its face incorrectly — eyes off-center, mouth spiraling vertically. Behind it, statues in glitched worship positions stutter. Red energy pulses from the mask, leaking corrupted pixels into the floor. Visual/Style Tags: +glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion Txt2Img Prompt: glitch mask floating midair with red energy pulses, incorrect facial rendering, statues frozen in glitched postures, pixel blood leaking downward, +glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion Negative Prompt: normal mask, symmetrical face, mythic clean statues, polished scene Hashtags: \#RedloopEntity #MaskOfGlitch #SigilDistortion #FaceCorruption #EchoedWorship --- (Five more prompts will follow in next message due to length constraints — continuing below.)
John6666/lemonsaltmix-v12-sdxl	John6666	"2025-05-05T12:13:17Z"	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "background", "eyes", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:finetune:Laxhar/noobai-XL-1.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2025-05-05T12:07:37Z"	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - background - eyes - noobai - illustrious base_model: Laxhar/noobai-XL-1.1 --- Original model is [here](https://civitai.com/models/1522819/lemonsaltmix?modelVersionId=1748955). This model created by [oritatami_neko](https://civitai.com/user/oritatami_neko).
sugilee/mental-bert-base-multiclass-linear-20	sugilee	"2025-05-05T11:59:33Z"	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-05-05T11:58:54Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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furkangomleksiz/youtube-spam-detector-model	furkangomleksiz	"2025-05-05T11:57:48Z"	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-05-05T11:56:33Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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unsloth/gemma-3-4b-it	unsloth	"2025-05-05T11:38:54Z"	36,929	9	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "unsloth", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	"2025-03-12T08:42:15Z"	--- tags: - unsloth license: gemma library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: - google/gemma-3-4b-it --- # Gemma 3 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/core) Resources and Technical Documentation: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Terms of Use: [Terms][terms] Authors: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - Input: - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - Output: - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API You can initialize the model and processor for inference with `pipeline` as follows. ```python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="google/gemma-3-4b-it", device="cuda", torch_dtype=torch.bfloat16 ) ``` With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. ```python messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] } ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) # Okay, let's take a look! # Based on the image, the animal on the candy is a turtle. # You can see the shell shape and the head and legs. ``` #### Running the model on a single/multi GPU ```python # pip install accelerate from transformers import AutoProcessor, Gemma3ForConditionalGeneration from PIL import Image import requests import torch model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, {"type": "text", "text": "Describe this image in detail."} ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.bfloat16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) # Overall Impression:** The image is a close-up shot of a vibrant garden scene, # focusing on a cluster of pink cosmos flowers and a busy bumblebee. # It has a slightly soft, natural feel, likely captured in daylight. ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality \| Benchmark \| Metric \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:--------------:\|:-------------:\|:--------------:\|:--------------:\| \| [HellaSwag][hellaswag] \| 10-shot \| 62.3 \| 77.2 \| 84.2 \| 85.6 \| \| [BoolQ][boolq] \| 0-shot \| 63.2 \| 72.3 \| 78.8 \| 82.4 \| \| [PIQA][piqa] \| 0-shot \| 73.8 \| 79.6 \| 81.8 \| 83.3 \| \| [SocialIQA][socialiqa] \| 0-shot \| 48.9 \| 51.9 \| 53.4 \| 54.9 \| \| [TriviaQA][triviaqa] \| 5-shot \| 39.8 \| 65.8 \| 78.2 \| 85.5 \| \| [Natural Questions][naturalq] \| 5-shot \| 9.48 \| 20.0 \| 31.4 \| 36.1 \| \| [ARC-c][arc] \| 25-shot \| 38.4 \| 56.2 \| 68.9 \| 70.6 \| \| [ARC-e][arc] \| 0-shot \| 73.0 \| 82.4 \| 88.3 \| 89.0 \| \| [WinoGrande][winogrande] \| 5-shot \| 58.2 \| 64.7 \| 74.3 \| 78.8 \| \| [BIG-Bench Hard][bbh] \| few-shot \| 28.4 \| 50.9 \| 72.6 \| 77.7 \| \| [DROP][drop] \| 1-shot \| 42.4 \| 60.1 \| 72.2 \| 77.2 \| [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code \| Benchmark \| Metric \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|----------------\|:-------------:\|:--------------:\|:--------------:\| \| [MMLU][mmlu] \| 5-shot \| 59.6 \| 74.5 \| 78.6 \| \| [MMLU][mmlu] (Pro COT) \| 5-shot \| 29.2 \| 45.3 \| 52.2 \| \| [AGIEval][agieval] \| 3-5-shot \| 42.1 \| 57.4 \| 66.2 \| \| [MATH][math] \| 4-shot \| 24.2 \| 43.3 \| 50.0 \| \| [GSM8K][gsm8k] \| 8-shot \| 38.4 \| 71.0 \| 82.6 \| \| [GPQA][gpqa] \| 5-shot \| 15.0 \| 25.4 \| 24.3 \| \| [MBPP][mbpp] \| 3-shot \| 46.0 \| 60.4 \| 65.6 \| \| [HumanEval][humaneval] \| 0-shot \| 36.0 \| 45.7 \| 48.8 \| [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual \| Benchmark \| Gemma 3 PT 1B \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------------ \|:-------------:\|:-------------:\|:--------------:\|:--------------:\| \| [MGSM][mgsm] \| 2.04 \| 34.7 \| 64.3 \| 74.3 \| \| [Global-MMLU-Lite][global-mmlu-lite] \| 24.9 \| 57.0 \| 69.4 \| 75.7 \| \| [WMT24++][wmt24pp] (ChrF) \| 36.7 \| 48.4 \| 53.9 \| 55.7 \| \| [FloRes][flores] \| 29.5 \| 39.2 \| 46.0 \| 48.8 \| \| [XQuAD][xquad] (all) \| 43.9 \| 68.0 \| 74.5 \| 76.8 \| \| [ECLeKTic][eclektic] \| 4.69 \| 11.0 \| 17.2 \| 24.4 \| \| [IndicGenBench][indicgenbench] \| 41.4 \| 57.2 \| 61.7 \| 63.4 \| [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal \| Benchmark \| Gemma 3 PT 4B \| Gemma 3 PT 12B \| Gemma 3 PT 27B \| \| ------------------------------ \|:-------------:\|:--------------:\|:--------------:\| \| [COCOcap][coco-cap] \| 102 \| 111 \| 116 \| \| [DocVQA][docvqa] (val) \| 72.8 \| 82.3 \| 85.6 \| \| [InfoVQA][info-vqa] (val) \| 44.1 \| 54.8 \| 59.4 \| \| [MMMU][mmmu] (pt) \| 39.2 \| 50.3 \| 56.1 \| \| [TextVQA][textvqa] (val) \| 58.9 \| 66.5 \| 68.6 \| \| [RealWorldQA][realworldqa] \| 45.5 \| 52.2 \| 53.9 \| \| [ReMI][remi] \| 27.3 \| 38.5 \| 44.8 \| \| [AI2D][ai2d] \| 63.2 \| 75.2 \| 79.0 \| \| [ChartQA][chartqa] \| 63.6 \| 74.7 \| 76.3 \| \| [VQAv2][vqav2] \| 63.9 \| 71.2 \| 72.9 \| \| [BLINK][blinkvqa] \| 38.0 \| 35.9 \| 39.6 \| \| [OKVQA][okvqa] \| 51.0 \| 58.7 \| 60.2 \| \| [TallyQA][tallyqa] \| 42.5 \| 51.8 \| 54.3 \| \| [SpatialSense VQA][ss-vqa] \| 50.9 \| 60.0 \| 59.4 \| \| [CountBenchQA][countbenchqa] \| 26.1 \| 17.8 \| 68.0 \| [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - Child Safety: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - Content Safety: Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - Representational Harms: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - Privacy violations: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805
ustc-community/dfine-large-coco	ustc-community	"2025-05-05T11:35:56Z"	68	0	transformers	[ "transformers", "safetensors", "d_fine", "object-detection", "vision", "en", "dataset:coco", "arxiv:2410.13842", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	"2025-02-22T12:04:35Z"	--- library_name: transformers license: apache-2.0 language: - en pipeline_tag: object-detection tags: - object-detection - vision datasets: - coco --- ## D-FINE ### Overview The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf) This is the HF transformers implementation for D-FINE _coco -> model trained on COCO _obj365 -> model trained on Object365 _obj2coco -> model trained on Object365 and then finetuned on COCO ### Performance D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). ![COCO.png](https://huggingface.co/datasets/vladislavbro/images/resolve/main/COCO.PNG) ### How to use ```python import torch import requests from PIL import Image from transformers import DFineForObjectDetection, AutoImageProcessor url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-large-coco") model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-large-coco") inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(inputs) results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3) for result in results: for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]): score, label = score.item(), label_id.item() box = [round(i, 2) for i in box.tolist()] print(f"{model.config.id2label[label]}: {score:.2f} {box}") ``` ### Training D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios. ### Applications D-FINE is ideal for real-time object detection in diverse applications such as autonomous driving, surveillance systems, robotics, and retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments.
Sim4Rec/inter-play-sim-assistant-sft	Sim4Rec	"2025-05-05T11:24:44Z"	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-02T00:05:41Z"	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: inter-play-sim-assistant-sft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for inter-play-sim-assistant-sft This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Sim4Rec/inter-play-sim-assistant-sft", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jerome-ramos-20/huggingface/runs/lzvwgcqb) This model was trained with SFT. ### Framework versions - TRL: 0.14.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.0.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF	Triangle104	"2025-05-05T10:59:32Z"	0	0	transformers	[ "transformers", "gguf", "nvidia", "math", "llama-cpp", "gguf-my-repo", "en", "dataset:nvidia/OpenMathReasoning", "base_model:nvidia/OpenMath-Nemotron-7B", "base_model:quantized:nvidia/OpenMath-Nemotron-7B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-05-05T10:57:22Z"	--- base_model: nvidia/OpenMath-Nemotron-7B datasets: - nvidia/OpenMathReasoning language: - en library_name: transformers license: cc-by-4.0 tags: - nvidia - math - llama-cpp - gguf-my-repo --- # Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/OpenMath-Nemotron-7B`](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) for more details on the model. --- OpenMath-Nemotron-7B is created by finetuning Qwen/Qwen2.5-Math-7B on OpenMathReasoning dataset. This model is ready for commercial use. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -c 2048 ```
mlgawd/cyberrealistic_pony	mlgawd	"2025-05-05T10:57:23Z"	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2025-05-05T10:55:33Z"	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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MrRobotoAI/114-Q4_K_M-GGUF	MrRobotoAI	"2025-05-05T10:43:27Z"	41	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/114", "base_model:quantized:MrRobotoAI/114", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-05-05T10:43:06Z"	--- base_model: MrRobotoAI/114 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/114-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/114`](https://huggingface.co/MrRobotoAI/114) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/MrRobotoAI/114) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -c 2048 ```
tori29umai/FramePackI2V_HY_rotate_indoor_F1	tori29umai	"2025-05-05T10:03:44Z"	0	0	diffusers	[ "diffusers", "safetensors", "license:other", "region:us" ]	null	"2025-05-05T09:53:58Z"	--- license: other license_name: other license_link: https://huggingface.co/lllyasviel/FramePack_F1_I2V_HY_20250503 ---
jssky/61de34b2-2cbe-474f-9759-7579b3f05a6a	jssky	"2025-05-05T09:56:11Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "generated_from_trainer", "conversational", "base_model:NousResearch/Llama-2-7b-hf", "base_model:finetune:NousResearch/Llama-2-7b-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-05-05T09:40:09Z"	--- library_name: transformers base_model: NousResearch/Llama-2-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: 61de34b2-2cbe-474f-9759-7579b3f05a6a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.9.0` ```yaml base_model: NousResearch/Llama-2-7b-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 012ab4813cc99fb8_train_data.json ds_type: json format: custom path: /workspace/input_data/012ab4813cc99fb8_train_data.json type: field_input: evidence field_instruction: question field_output: SQL format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: jssky/61de34b2-2cbe-474f-9759-7579b3f05a6a hub_repo: null hub_strategy: checkpoint hub_token: null huggingface_repo_visibility: public learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lr_scheduler: cosine max_steps: 1000 micro_batch_size: 2 mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 512 strict: false tf32: false train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b1e23278-252e-44d7-9491-1b28d344421c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b1e23278-252e-44d7-9491-1b28d344421c warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 61de34b2-2cbe-474f-9759-7579b3f05a6a This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 594 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0051 \| 1 \| 1.0069 \| \| 0.8986 \| 0.2525 \| 50 \| 0.9070 \| \| 0.732 \| 0.5051 \| 100 \| 0.7708 \| \| 0.6974 \| 0.7576 \| 150 \| 0.7275 \| \| 0.6078 \| 1.0101 \| 200 \| 0.6594 \| \| 0.401 \| 1.2626 \| 250 \| 0.5793 \| \| 0.3573 \| 1.5152 \| 300 \| 0.5292 \| \| 0.2894 \| 1.7677 \| 350 \| 0.4295 \| \| 0.2011 \| 2.0202 \| 400 \| 0.3899 \| \| 0.1267 \| 2.2727 \| 450 \| 0.3806 \| \| 0.1095 \| 2.5253 \| 500 \| 0.3797 \| \| 0.1124 \| 2.7778 \| 550 \| 0.3758 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
hf-100/Mistral-Large-Instruct-2411-Spellbound-StoryWriter-123B-instruct-0.1-instruct-chkpt-80-adapter	hf-100	"2025-05-05T09:50:46Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-05-05T09:33:36Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mcity-data-engine/fisheye8k_embedding_detection_transformer_torch	mcity-data-engine	"2025-05-05T09:11:01Z"	0	0	null	[ "dataset:Voxel51/fisheye8k", "arxiv:2504.21614", "region:us" ]	null	"2025-02-10T22:44:10Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
aparajitha11/hand-gcn-word	aparajitha11	"2025-05-05T09:09:25Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T09:09:20Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
ghaniashafiqa/FT-Deepseek-7b-Adapter	ghaniashafiqa	"2025-05-05T09:06:16Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-05-05T09:05:57Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
Don042/Test-run	Don042	"2025-05-05T09:01:27Z"	0	0	null	[ "license:openrail", "region:us" ]	null	"2025-05-05T09:01:17Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
bevinv/results	bevinv	"2025-05-05T08:54:23Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T08:54:20Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
baby-dev/4e9ed59d-cee2-42f9-b637-f3bcc0abc082	baby-dev	"2025-05-05T08:51:36Z"	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "region:us" ]	null	"2025-05-05T08:51:09Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
RichardErkhov/hemanth955_-_Legal-Mistral-gguf	RichardErkhov	"2025-05-05T08:43:38Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T08:43:38Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
Ideapad/qa_entropy_all_1e-05_full_5_forget10_layer-15-before_linear	Ideapad	"2025-05-05T08:43:32Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T08:43:32Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
karanpar/loraproject	karanpar	"2025-05-05T08:43:07Z"	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2025-05-05T08:43:07Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step120-reward	DPO-RM	"2025-05-05T08:42:13Z"	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	"2025-05-05T08:40:51Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
risolmayo/0446cd97-6f35-44df-bb7f-c6f9c3a956dd	risolmayo	"2025-05-05T08:42:09Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T08:33:22Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
prakanda/Audio_Features_BiLSTM	prakanda	"2025-05-05T08:33:16Z"	0	0	null	[ "region:us" ]	null	"2025-02-06T12:45:30Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step90-actor	DPO-RM	"2025-05-05T08:28:07Z"	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	"2025-05-05T08:26:52Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step80-actor	DPO-RM	"2025-05-05T08:23:58Z"	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	"2025-05-05T08:22:41Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step70-reward	DPO-RM	"2025-05-05T08:21:17Z"	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	"2025-05-05T08:20:06Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
insidious316/llama-test	insidious316	"2025-05-05T08:00:14Z"	0	0	null	[ "safetensors", "llama", "region:us" ]	null	"2025-05-05T07:11:42Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
Minasanjotaro/270180c0-57a1-4947-9545-c96aa4b77dc2	Minasanjotaro	"2025-05-05T08:00:08Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T07:24:04Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
albertus-sussex/veriscrape-fixed-simcse-auto-reference_2_to_verify_8-fold-7	albertus-sussex	"2025-05-05T07:53:29Z"	11	0	transformers	[ "transformers", "safetensors", "roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	"2025-04-01T12:31:24Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>
lesso02/a5fc37b5-9676-49df-b8a9-d95f8d8c081b	lesso02	"2025-05-05T07:46:22Z"	0	0	null	[ "region:us" ]	null	"2025-05-05T07:00:04Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

procit008/vits_dutch_female_v0.2

procit008

"2025-05-05T21:18:52Z"

0

transformers

[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-05-05T21:18:38Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF

mradermacher

"2025-05-05T21:18:05Z"

0

transformers

[ "transformers", "gguf", "grpo,", "biomed,reasoning", "en", "dataset:openlifescienceai/medmcqa", "base_model:rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B", "base_model:quantized:rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-05-05T21:10:18Z"

--- base_model: rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B datasets: - openlifescienceai/medmcqa language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - grpo, - biomed,reasoning --- ## About      weighted/imatrix quants of https://huggingface.co/rgb2gbr/GRPO_BioMedmcqa_Qwen2.5-0.5B  static quants are available at https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/GRPO_BioMedmcqa_Qwen2.5-0.5B-i1-GGUF/resolve/main/GRPO_BioMedmcqa_Qwen2.5-0.5B.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

NMolby/profile-pictures

NMolby

"2025-05-05T21:05:21Z"

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-05-05T20:41:02Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nico --- # Profile Pictures <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nico` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nico", "lora_weights": "https://huggingface.co/NMolby/profile-pictures/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('NMolby/profile-pictures', weight_name='lora.safetensors') image = pipeline('nico').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/NMolby/profile-pictures/discussions) to add images that show off what you’ve made with this LoRA.

kessinger/dominic

kessinger

"2025-05-05T20:58:02Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-05-05T20:58:02Z"

--- license: apache-2.0 ---

rudrashah/RLM_hingu

rudrashah

"2025-05-05T20:45:42Z"

0

2

keras-hub

[ "keras-hub", "hinglish", "text-generation", "hi", "en", "dataset:Abhishekcr448/Hinglish-Everyday-Conversations-1M", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "license:mit", "region:us" ]

text-generation

"2025-05-05T15:32:04Z"

--- license: mit datasets: - Abhishekcr448/Hinglish-Everyday-Conversations-1M language: - hi - en base_model: - google/gemma-3-1b-it pipeline_tag: text-generation tags: - hinglish library_name: keras-hub --- # RLM_hingu <p align="center"> <a href="https://www.buymeacoffee.com/rudrashah" target="_blank"> <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;"> </a> </p> **RLM_hingu** is a fine-tuned version of the [Gemma-3B Instruct](https://huggingface.co/google/gemma-1.1-1b-it) model, adapted for casual Hinglish (Hindi-English) conversation using the `keras-nlp` framework. It is designed for lightweight conversational tasks in Hinglish, optimized with the `JAX` backend for efficiency. ## Model Overview - **Base model**: `gemma3_instruct_1b` - **Library**: [`keras-nlp`](https://github.com/keras-team/keras-nlp) - **Backend**: JAX (recommended for best performance) - **Sampling Method**: Top-K (k=10) - **Use Case**: Conversational Hinglish response generation ## Usage ``` python from keras_nlp.models import Gemma3CausalLM from keras_nlp.samplers import TopKSampler model = Gemma3CausalLM.from_preset("hf://rudrashah/RLM_hingu") template = "Question:\n{question}\n\nAnswer:\n{answer}" prompt = template.format( question="Rudra acha ladka hai?", answer="", ) output = model.generate(prompt, max_length=256) print(output) ``` ```output Question: Rudra acha ladka hai? Answer: haan, sabse best hai. ``` To run RLM_hingu, just paste this code and wait.

silviasapora/gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140

silviasapora

"2025-05-05T20:39:34Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "endpoints_compatible", "region:us" ]

null

"2025-05-05T20:17:05Z"

--- library_name: transformers model_name: gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="silviasapora/gemma-7b-sft-shuffled-dpo-shuffled-5e-7-005-v140", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/silvias/huggingface/runs/7fsvu452) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.2.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

li-muyang/zephyr-7b-sft-full

li-muyang

"2025-05-05T20:28:02Z"

11

0

transformers

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-04T10:52:36Z"

--- library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: zephyr-7b-sft-full results: [] ---  # zephyr-7b-sft-full This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.9286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.977 | 0.0923 | 100 | 0.9750 | | 0.9604 | 0.1846 | 200 | 0.9686 | | 0.961 | 0.2769 | 300 | 0.9619 | | 0.9426 | 0.3692 | 400 | 0.9555 | | 0.9585 | 0.4615 | 500 | 0.9498 | | 0.9288 | 0.5538 | 600 | 0.9442 | | 0.9138 | 0.6461 | 700 | 0.9394 | | 0.9061 | 0.7383 | 800 | 0.9351 | | 0.9229 | 0.8306 | 900 | 0.9314 | | 0.9099 | 0.9229 | 1000 | 0.9286 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+rocm6.2 - Datasets 3.5.0 - Tokenizers 0.20.3

chargoddard/mergekit-slerp-qdwxicz

chargoddard

"2025-05-05T20:24:22Z"

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch", "base_model:merge:arcee-ai/sec-mistral-7b-instruct-1.6-epoch", "base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02", "base_model:merge:cognitivecomputations/dolphin-2.8-mistral-7b-v02", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T20:19:50Z"

--- base_model: - cognitivecomputations/dolphin-2.8-mistral-7b-v02 - arcee-ai/sec-mistral-7b-instruct-1.6-epoch library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) * [arcee-ai/sec-mistral-7b-instruct-1.6-epoch](https://huggingface.co/arcee-ai/sec-mistral-7b-instruct-1.6-epoch) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: arcee-ai/sec-mistral-7b-instruct-1.6-epoch layer_range: [0, 32] - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 layer_range: [0, 32] merge_method: slerp base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

adamsdossantos/huggy

adamsdossantos

"2025-05-05T20:21:16Z"

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

"2025-05-05T20:21:10Z"

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: adamsdossantos/huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

art0123/ComfyUI_art

art0123

"2025-05-05T20:09:35Z"

0

19

null

[ "region:us" ]

null

"2024-09-05T05:20:23Z"

ComfyUI_art_v3 (Сборка) - Обновление! 🔧 ✨Изменения: ComfyUI и все узлы приведены к актуальным версиям по состоянию на 05.05.2025. Обновлен Pytorch до версии 2.7.0 + cuda128. Обновлен Xformers до версии 0.0.31.dev1030. ✨ Добавлены новые узлы. Полный список... AGSoft, cg-use-everywhere, ComfyUI_AdvancedRefluxControl, ComfyUI_bnb_nf4_fp4_Loaders, ComfyUI_Comfyroll_CustomNodes, comfyui_controlnet_aux, comfyui_custom_nodes_alekpet, comfyui_essentials, comfyui_faceanalysis, comfyui_instantid, comfyui_ipadapter_plus, comfyui_layerstyle, ComfyUI_LayerStyle_Advance, comfyui_patches_ll, comfyui_pulid_flux_ll, comfyui_segment_anything, comfyui_ttp_toolset, comfyui_ultimatesdupscale, comfyui_zenid, comfyui-art-venture, ComfyUI-AutoCropFaces, ComfyUI-Crystools, comfyui-custom-scripts, comfyui-detail-daemon, comfyui-enricos-nodes, comfyui-florence2, ComfyUI-Fluxtapoz, ComfyUI-FramePackWrapper, ComfyUI-GGUF, comfyui-ic-light, comfyui-impact-pack, comfyui-impact-subpack, comfyui-inpaint-cropandstitch, comfyui-inpaint-nodes, comfyui-inspire-pack, comfyui-kjnodes, comfyui-manager, comfyui-mxtoolkit, ComfyUI-PuLID-Flux-Enhanced, comfyui-reactor-node, comfyui-rmbg, ComfyUI-segment-anything-2, Comfyui-StableSR, comfyui-supir, ComfyUI-TiledDiffusion, comfyui-videohelpersuite, comfyui-wd14-tagger, efficiency-nodes-comfyui, facerestore_cf, pulid_comfyui, rgthree-comfy, teacache, was-node-suite-comfyui, wavespeed //......................................................................................... ComfyUI_art_v2 (Сборка) Что нового: ComfyUI и все текущие узлы приведены к актуальным версиям по состоянию на 08.02.2025. Обновлен Pytorch до версии 2.6.0 + cuda126 Обновлен Xformers до версии 0.0.29.post2 Добавлены новые узлы: Comfy-WaveSpeed, ComfyUI-TeaCache, ComfyUI_Patches_ll, ComfyUI-SUPIR, comfyui_ttp_toolset, comfyui-detail-daemon, comfyui_zenid //......................................................................................... ComfyUI_art_v1 (Сборка) Приветствую всех, кто любит экспериментировать с нейросетями и создавать уникальные арты! Сегодня я хочу представить вам свою сборку ComfyUI, которая станет вашим надежным помощником в мире цифрового творчества. Что внутри? В сборку интегрированы стабильные версии ключевых компонентов: Pytorch, Xformers, Insightface, Onnxruntime, Facexlib, Dlib и Bitsandbytes. Также добавлены все необходимые модели для работы с Реактором. Для вашего удобства в сборку включены полезные пакеты нод, которые значительно расширяют возможности ComfyUI: ComfyUI-Manager, Comfyui-Crystools, Rgthree-Comfy, ComfyUI-Custom-Scripts, Comfyui-Kjnodes, ComfyUI Impact Pack, ComfyUI Impact Subpack, ComfyUI Inspire Pack, Cg-use-everywhere, ComfyUI-Easy-Use, Comfyroll_CustomNodes, ComfyUI_Custom_Nodes_AlekPet, Comfyui-Reactor-node, ComfyUI-GGUF, Comfyui_bnb_nf4_loaders, ComfyUI_IPAdapter_plus, ComfyUI_InstantID, ComfyUI_essentials, ComfyUI_FaceAnalysis, PuLID_ComfyUI, ComfyUI_PuLID_Flux_ll, ComfyUI-Florence2, ComfyUI-AutoCropFaces, Comfyui_controlnet_aux, ComfyUI_AdvancedRefluxControl, ComfyUI_UltimateSDUpscale, Comfyui-inpaint-nodes, ComfyUI-Inpaint-CropAndStitch, ComfyUI-Fluxtapoz, ComfyUI-mxToolkit, ComfyUI-RMBG, ComfyUI ArtVenture, ComfyUI-enricos-nodes, ComfyUI-TiledDiffusion. Как установить? 1. Просто распакуйте архив. 2. Для запуска используйте run_nvidia_gpu.bat. Как обновить. 1. Перейдите в папку Install. 2. Запустите батник update_ComfyUI.bat, чтобы обновить ComfyUI до актуальной версии. В папке Install также найдете дополнительные батники для выборочной установки компонентов или переустановки сломанных элементов. Если вы используете другую сборку ComfyUI, просто скопируйте папку Install в корень вашей ComfyUI, и пользуйтесь на здоровье! В заключение Эта сборка создана специально для тех, кто только начинает свой путь в мире нейросетей и цифрового искусства. Я постарался сделать всё возможное, чтобы ваше знакомство с ComfyUI было максимально простым и комфортным. Всё, что вам нужно, уже интегрировано и настроено – просто распакуйте архив, следуйте инструкциям, и вы сможете сразу приступить к созданию своих первых шедевров. Надеюсь, что ComfyUI станет вашим надежным помощником, вдохновит на эксперименты и поможет раскрыть ваш творческий потенциал. Не бойтесь пробовать новое, задавать вопросы и делиться своими результатами! Удачи в творчестве, и пусть ваши идеи оживают с помощью этой сборки! 🎨✨

MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetune-v3

MohamedAhmedAE

"2025-05-05T20:03:58Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-04-20T22:48:01Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlfoundations-dev/d1_code_fasttext_3k

mlfoundations-dev

"2025-05-05T20:03:13Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T12:14:15Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_fasttext_3k results: [] ---  # d1_code_fasttext_3k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_fasttext_3k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

mluger/vitFaceExpressionBalancedFocalLoss

mluger

"2025-05-05T19:56:52Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-04-23T14:12:20Z"

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vitFaceExpressionBalancedFocalLoss results: [] ---  # vitFaceExpressionBalancedFocalLoss This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2569 - Accuracy: 0.6952 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4251 | 1.0 | 898 | 0.3174 | 0.6183 | | 0.2667 | 2.0 | 1796 | 0.2676 | 0.6573 | | 0.205 | 3.0 | 2694 | 0.2580 | 0.6776 | | 0.1659 | 4.0 | 3592 | 0.2674 | 0.6826 | | 0.1374 | 5.0 | 4490 | 0.2647 | 0.6885 | | 0.1068 | 6.0 | 5388 | 0.2573 | 0.6935 | | 0.0881 | 7.0 | 6286 | 0.2581 | 0.6982 | | 0.0832 | 8.0 | 7184 | 0.2569 | 0.6952 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1

YassineeM/tiktok

YassineeM

"2025-05-05T19:49:12Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-05-05T19:49:12Z"

--- license: apache-2.0 ---

Theleebebe/my-zoom-fb-validation-model

Theleebebe

"2025-05-05T19:44:07Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-05-05T19:43:33Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

alelisita/reic

alelisita

"2025-05-05T19:28:14Z"

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-05-05T19:16:26Z"

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Reic <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/alelisita/reic/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('alelisita/reic', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/alelisita/reic/discussions) to add images that show off what you’ve made with this LoRA.

MinaMila/Phi3_unlearned_0.5_0.25_0.5

MinaMila

"2025-05-05T19:19:54Z"

0

transformers

[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T19:16:44Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

xbilek25/whisper-medium-en-cv-8.0

xbilek25

"2025-05-05T19:14:09Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:mozilla-foundation/common_voice_17_0", "base_model:openai/whisper-medium.en", "base_model:finetune:openai/whisper-medium.en", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2025-05-05T17:25:46Z"

--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-medium.en tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_17_0 metrics: - wer model-index: - name: whisper-medium-en-cv-8.0 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 17.0 type: mozilla-foundation/common_voice_17_0 args: 'config: en, split: test' metrics: - name: Wer type: wer value: 22.90263319044703 ---  # whisper-medium-en-cv-8.0 This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7352 - Wer: 22.9026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 375 - training_steps: 2250 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | No log | 0 | 0 | 2.0556 | 32.3025 | | 0.5507 | 0.1667 | 375 | 0.7920 | 25.9032 | | 0.3861 | 0.3333 | 750 | 0.7215 | 24.6479 | | 0.205 | 1.1667 | 1125 | 0.7130 | 22.8108 | | 0.1431 | 1.3333 | 1500 | 0.7193 | 23.8212 | | 0.0802 | 2.1667 | 1875 | 0.7302 | 23.5150 | | 0.0626 | 2.3333 | 2250 | 0.7352 | 22.9026 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1

Liyusan-sondra/Full.liyumisa.emo.video.viral.la.nina.emo.video.liyusan.sondra.video.viral

Liyusan-sondra

"2025-05-05T19:11:04Z"

0

null

[ "region:us" ]

null

"2025-05-05T19:10:35Z"

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Liyusan-sondra) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Liyusan-sondra) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Liyusan-sondra)

vivnatan/legal-ft-20c85cc6-30d1-49ca-97e6-cce1045a4b4a

vivnatan

"2025-05-05T19:09:43Z"

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:780", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

"2025-05-05T19:08:28Z"

--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:780 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: What is one potential negative use of LLMs related to education? sentences: - 'A year ago, the only organization that had released a generally useful LLM was OpenAI. We’ve now seen better-than-GPT-3 class models produced by Anthropic, Mistral, Google, Meta, EleutherAI, Stability AI, TII in Abu Dhabi (Falcon), Microsoft Research, xAI, Replit, Baidu and a bunch of other organizations. The training cost (hardware and electricity) is still significant—initially millions of dollars, but that seems to have dropped to the tens of thousands already. Microsoft’s Phi-2 claims to have used “14 days on 96 A100 GPUs”, which works out at around $35,000 using current Lambda pricing.' - 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for this feature, driven by users building the same interactive app twice with two different models and voting on the answer. Hard to come up with a more convincing argument that this feature is now a commodity that can be effectively implemented against all of the leading models. I’ve been tinkering with a version of this myself for my Datasette project, with the goal of letting users use prompts to build and iterate on custom widgets and data visualizations against their own data. I also figured out a similar pattern for writing one-shot Python programs, enabled by uv.' - 'Here’s the sequel to this post: Things we learned about LLMs in 2024. Large Language Models In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software. LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code. They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.' - source_sentence: What is the significance of the UK’s Railway Mania in the context of railway construction? sentences: - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'The top five: ai (342), generativeai (300), llms (287), openai (86), chatgpt (78). I’ve written a lot about this stuff! I grabbed a screenshot of my Plausible analytics for the year, fed that to ChatGPT Vision, told it to extract the data into a table, then got it to mix in entry titles (from a SQL query it wrote) and produced this table with it. Here are my top entries this year by amount of traffic: Article Visitors Pageviews Bing: “I will not harm you unless you harm me first” 1.1M 1.3M Leaked Google document: “We Have No Moat, And Neither Does OpenAI” 132k 162k Large language models are having their Stable Diffusion moment 121k 150k Prompt injection: What’s the worst that can happen? 79.8k 95.9k' - 'Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people? As the quality of results produced by AI models has increased over the year, these questions have become even more pressing. The impact on human society in terms of these models is already huge, if difficult to objectively measure. People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators. There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic. My blog in 2023 Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):' - source_sentence: Can you build an LLM without the right data? Why or why not? sentences: - 'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version! What matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is. If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.' - 'Embeddings: What they are and why they matter 61.7k 79.3k Catching up on the weird world of LLMs 61.6k 85.9k llamafile is the new best way to run an LLM on your own computer 52k 66k Prompt injection explained, with video, slides, and a transcript 51k 61.9k AI-enhanced development makes me more ambitious with my projects 49.6k 60.1k Understanding GPT tokenizers 49.5k 61.1k Exploring GPTs: ChatGPT in a trench coat? 46.4k 58.5k Could you train a ChatGPT-beating model for $85,000 and run it in a browser? 40.5k 49.2k How to implement Q&A against your documentation with GPT3, embeddings and Datasette 37.3k 44.9k Lawyer cites fake cases invented by ChatGPT, judge is not amused 37.1k 47.4k' - 'One way to think about these models is an extension of the chain-of-thought prompting trick, first explored in the May 2022 paper Large Language Models are Zero-Shot Reasoners. This is that trick where, if you get a model to talk out loud about a problem it’s solving, you often get a result which the model would not have achieved otherwise. o1 takes this process and further bakes it into the model itself. The details are somewhat obfuscated: o1 models spend “reasoning tokens” thinking through the problem that are not directly visible to the user (though the ChatGPT UI shows a summary of them), then outputs a final result.' - source_sentence: What was the estimated training cost for DeepSeek v3? sentences: - 'Now that those features are rolling out they’re pretty weak. As an LLM power-user I know what these models are capable of, and Apple’s LLM features offer a pale imitation of what a frontier LLM can do. Instead we’re getting notification summaries that misrepresent news headlines and writing assistant tools that I’ve not found useful at all. Genmoji are kind of fun though. The rise of inference-scaling “reasoning” models The most interesting development in the final quarter of 2024 was the introduction of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as o1-preview and o1-mini on September 12th.' - 'Prompt injection is a natural consequence of this gulibility. I’ve seen precious little progress on tackling that problem in 2024, and we’ve been talking about it since September 2022. I’m beginning to see the most popular idea of “agents” as dependent on AGI itself. A model that’s robust against gulliblity is a very tall order indeed. Evals really matter Anthropic’s Amanda Askell (responsible for much of the work behind Claude’s Character):' - 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed models currently available, significantly bigger than the largest of Meta’s Llama series, Llama 3.1 405B. Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models. This is by far the highest ranking openly licensed model. The really impressive thing about DeepSeek v3 is the training cost. The model was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model that benchmarks slightly worse.' - source_sentence: How does the author describe the clarity of the term "agents"? sentences: - 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do. That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro. Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.' - 'Things we learned about LLMs in 2024 Simon Willison’s Weblog Subscribe Things we learned about LLMs in 2024 31st December 2024 A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. This is a sequel to my review of 2023. In this article:' - '“Agents” still haven’t really happened yet I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that. If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.8416666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.975 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9916666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8416666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32499999999999996 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19833333333333336 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8416666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.975 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9916666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.933195287437209 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9105555555555555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9105555555555557 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("vivnatan/legal-ft-20c85cc6-30d1-49ca-97e6-cce1045a4b4a") # Run inference sentences = [ 'How does the author describe the clarity of the term "agents"?', '“Agents” still haven’t really happened yet\nI find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.\nIf you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.', 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do.\nThat era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro.\nSince the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.8417 | | cosine_accuracy@3 | 0.975 | | cosine_accuracy@5 | 0.9917 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 0.8417 | | cosine_precision@3 | 0.325 | | cosine_precision@5 | 0.1983 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.8417 | | cosine_recall@3 | 0.975 | | cosine_recall@5 | 0.9917 | | cosine_recall@10 | 1.0 | | **cosine_ndcg@10** | **0.9332** | | cosine_mrr@10 | 0.9106 | | cosine_map@100 | 0.9106 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 780 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 780 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 17.51 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.32 tokens</li><li>max: 214 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What is the main discovery about Large Language Models (LLMs) in the past 24-36 months?</code> | <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> | | <code>What resources are used to create Large Language Models?</code> | <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> | | <code>What are some of the capabilities of LLMs mentioned in the context?</code> | <code>Here’s the sequel to this post: Things we learned about LLMs in 2024.<br>Large Language Models<br>In the past 24-36 months, our species has discovered that you can take a GIANT corpus of text, run it through a pile of GPUs, and use it to create a fascinating new kind of software.<br>LLMs can do a lot of things. They can answer questions, summarize documents, translate from one language to another, extract information and even write surprisingly competent code.<br>They can also help you cheat at your homework, generate unlimited streams of fake content and be used for all manner of nefarious purposes.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs | Epoch | Step | Training Loss | cosine_ndcg@10 | |:------:|:----:|:-------------:|:--------------:| | 0.6410 | 50 | - | 0.9199 | | 1.0 | 78 | - | 0.9322 | | 1.2821 | 100 | - | 0.9351 | | 1.9231 | 150 | - | 0.9415 | | 2.0 | 156 | - | 0.9434 | | 2.5641 | 200 | - | 0.9438 | | 3.0 | 234 | - | 0.9404 | | 3.2051 | 250 | - | 0.9432 | | 3.8462 | 300 | - | 0.9345 | | 4.0 | 312 | - | 0.9356 | | 4.4872 | 350 | - | 0.9393 | | 5.0 | 390 | - | 0.9358 | | 5.1282 | 400 | - | 0.9316 | | 5.7692 | 450 | - | 0.9335 | | 6.0 | 468 | - | 0.9350 | | 6.4103 | 500 | 1.3465 | 0.9412 | | 7.0 | 546 | - | 0.9375 | | 7.0513 | 550 | - | 0.9406 | | 7.6923 | 600 | - | 0.9410 | | 8.0 | 624 | - | 0.9375 | | 8.3333 | 650 | - | 0.9333 | | 8.9744 | 700 | - | 0.9375 | | 9.0 | 702 | - | 0.9375 | | 9.6154 | 750 | - | 0.9332 | | 10.0 | 780 | - | 0.9332 | ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Jobz-Hunting-Sajal-Malik-l/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.original.X-New

Jobz-Hunting-Sajal-Malik-l

"2025-05-05T19:08:57Z"

0

null

[ "region:us" ]

null

"2025-05-05T19:02:28Z"

[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Jobz-Hunting) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Jobz-Hunting) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Jobz-Hunting)

MrRobotoAI/A6

MrRobotoAI

"2025-05-05T18:48:39Z"

34

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:MrRobotoAI/A4", "base_model:merge:MrRobotoAI/A4", "base_model:MrRobotoAI/A5", "base_model:merge:MrRobotoAI/A5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T06:19:57Z"

--- base_model: - MrRobotoAI/A5 - MrRobotoAI/A4 library_name: transformers tags: - mergekit - merge --- # merge 13,500 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A5](https://huggingface.co/MrRobotoAI/A5) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A4](https://huggingface.co/MrRobotoAI/A4) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A5 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.6, 0.7, 0.8, 0.8, 0.8, 0.7, 0.6, 0.8, 0.8] - value: 1 - model: MrRobotoAI/A4 parameters: weight: - filter: v_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: o_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: up_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: gate_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - filter: down_proj value: [0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.2, 0.3, 0.4, 0.2, 0.2] - value: 0 base_model: MrRobotoAI/A5 dtype: bfloat16 ```

A7medsala7/my_awesome_model

A7medsala7

"2025-05-05T18:39:39Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-05-05T17:46:14Z"

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] ---  # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2370 - Accuracy: 0.9311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2218 | 1.0 | 1563 | 0.1980 | 0.9254 | | 0.1431 | 2.0 | 3126 | 0.2370 | 0.9311 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1

Venkat423/Gemma-3-4b-finetuned-final

Venkat423

"2025-05-05T18:13:02Z"

0

null

[ "safetensors", "region:us" ]

null

"2025-05-05T10:18:32Z"

--- language: bn license: cc-by-nc-4.0 datasets: - https://www.kaggle.com/datasets/saurabhshahane/bangla-wikipedia - https://github.com/shuhanmirza/Bengali-Poem-Dataset --- # Gemma-3-4b Fine-tuned on Bengali Wikipedia and poem dataset

odedovadia/Qwen3-4B-chess-10K-single-move-sft-2025-05-05-red-1K-no-cot-checkpoint-240

odedovadia

"2025-05-05T17:36:34Z"

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T17:34:27Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

scb10x/typhoon2.1-gemma3-4b

scb10x

"2025-05-05T17:31:32Z"

7

0

null

[ "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:2412.13702", "license:gemma", "region:us" ]

text-generation

"2025-05-01T15:15:27Z"

--- license: gemma pipeline_tag: text-generation --- **Typhoon2.1-Gemma3-4B**: Thai Large Language Model (Instruct) **Typhoon2.1-Gemma3-4B** is a instruct Thai 🇹🇭 large language model with 4 billion parameters, a 128K context length, and function-calling capabilities. It is based on Gemma3 4B. Remark: This is text only model. We removed vision encoder for this version due to complexity. Stay-tune for version with vision encoder soon. ## **Performance** ![4b model performance](https://storage.googleapis.com/typhoon-public/assets/typhoon-21/performance4b_table.png) ## **Model Description** - **Model type**: A 4B instruct decoder-only model based on Gemma3 architecture. - **Requirement**: transformers 4.50.0 or newer. - **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧 - **License**: [Gemma License](https://github.com/google-deepmind/gemma/blob/main/LICENSE) ## Usage Example This code snippet shows how to use the Typhoon2.1-Gemma3-4B model for Thai or English text generation using the transformers library. It includes setting up the model and tokenizer, formatting chat messages in a system-user style, and generating a response. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "scb10x/typhoon2.1-gemma3-4b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a male AI assistant named Typhoon created by SCB 10X to be helpful, harmless, and honest. Typhoon is happy to help with analysis, question answering, math, coding, creative writing, teaching, role-play, general discussion, and all sorts of other tasks. Typhoon responds directly to all human messages without unnecessary affirmations or filler phrases like “Certainly!”, “Of course!”, “Absolutely!”, “Great!”, “Sure!”, etc. Specifically, Typhoon avoids starting responses with the word “Certainly” in any way. Typhoon follows this information in all languages, and always responds to the user in the language they use or request. Typhoon is now being connected with a human. Write in fluid, conversational prose, Show genuine interest in understanding requests, Express appropriate emotions and empathy. Also showing information in term that is easy to understand and visualized."}, {"role": "user", "content": "ขอสูตรไก่ย่าง"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=False # Switches between thinking and non-thinking modes. Default is False. ).to(model.device) outputs = model.generate( input_ids, max_new_tokens=512, do_sample=True, temperature=0.6, top_p=0.95, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Deploy as Server This section shows how to run Typhoon2.1 as an OpenAI-compatible API server using vllm. ```bash pip install vllm vllm serve scb10x/typhoon2.1-gemma3-4b --max-model-len 16000 --dtype bfloat16 --tool-call-parser pythonic --enable-auto-tool-choice # adjust --max-model-len based on your avaliable memory # you can use --quantization bitsandbytes to reduce the memory use while trade-off inference speed ``` ## Using Tools You can provide tools to the vLLM-powered OpenAI-compatible API for functionality. ``` from openai import OpenAI import json client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy") def get_weather(location: str, unit: str): return f"Getting the weather for {location} in {unit}..." tool_functions = {"get_weather": get_weather} tools = [{ "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} }, "required": ["location", "unit"] } } }] response = client.chat.completions.create( model=client.models.list().data[0].id, messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}], tools=tools, tool_choice="auto" ) tool_call = response.choices[0].message.tool_calls[0].function print(f"Function called: {tool_call.name}") print(f"Arguments: {tool_call.arguments}") print(f"Result: {get_weather(**json.loads(tool_call.arguments))}") ``` ## Switching Between Thinking and Non-Thinking Mode Typhoon supports two modes: Non-thinking mode (default): Fast response generation without extra reasoning steps. Thinking mode: The model first reasons internally, then provides a clearer and potentially more accurate final answer. You can enable thinking mode by: Setting enable_thinking=True in apply_chat_template. Using a special system prompt that instructs the model to reason inside <think>...</think> tags. You can turn on thinking mode by either - add enable_thinking=True to apply_chat_template ```python input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", enable_thinking=True # Switches between thinking and non-thinking modes. Default is False. ).to(model.device) ``` - manually by supply thinking mode system prompt ``` You are a helpful assistant. First, think through the reasoning internally, then present the reasoning within <think>...</think>. After thinking, clearly state a response that addresses the user's request and aligns with their preferences, not just providing a direct answer. ``` - in vllm powered openai compatible client you can add chat_template_kwargs to the post payload ```json { "model": "scb10x/typhoon2.1-gemma3-4b", "messages": [ {"role": "user", "content": "Give me a short introduction to large language models."} ], "chat_template_kwargs": {"enable_thinking": true} } ``` ## Budget forcing This section introduces budget forcing, an advanced technique to let the model spend more time and tokens reasoning before producing a final answer—great for improving performance on complex questions. ``` from vllm import LLM, SamplingParams from transformers import AutoTokenizer class BudgetForcingHandler: def __init__(self, model_name: str, max_think_token: int, max_ignore=5, temperature=0.6, seed=32): self.temperature = temperature self.seed = seed self.max_think_token = max_think_token self.max_ignore = max_ignore self.model = LLM(model_name, dtype='bfloat16', enforce_eager=True) self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.alternative_str = '\nAlternatively' self.system = """You are a reasoning assistant. First, think through the reasoning internally, then present the reasoning within <think>...</think>. After thinking, clearly state the final answer.""" def __call__(self, prompts: List[str]): count_prompt = len(prompts) prompts = [self.tokenizer.apply_chat_template([{'role': 'system', 'content': self.system}, {'role': 'user', 'content': f'Please solve this math question, and put your final answer within \\boxed{{}}.\n{p}'}], add_generation_prompt=True, tokenize=False) for p in prompts] sampling_params = SamplingParams( max_tokens=self.max_think_token, seed=self.seed, stop=["</think>"], skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( prompts, sampling_params=sampling_params ) outputs = [output.outputs[0].text for output in o] token_count = [len(output.outputs[0].token_ids) for output in o] for i in range(len(prompts)): prompts[i] = prompts[i] + outputs[i] for _ in range(self.max_ignore): # Num of times to skip stop token inference_loop_prompts = [] inference_idx = [] max_inference_token = 0 print('current token count: ', token_count) for i in range(len(prompts)): left_budget = self.max_think_token - token_count[i] if left_budget > 0: prompts[i] = prompts[i] + self.alternative_str inference_loop_prompts.append(prompts[i]) inference_idx.append(i) if left_budget > max_inference_token: max_inference_token = left_budget outputs = ['' for _ in range(len(prompts))] if max_inference_token == 0 or len(inference_loop_prompts) == 0: break sampling_params = SamplingParams( max_tokens=max_inference_token, min_tokens=1, seed=self.seed, stop=["</think>"], skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( inference_loop_prompts, sampling_params=sampling_params ) assert len(inference_idx) == len(inference_loop_prompts) assert len(inference_idx) == len(o) for i, output in zip(inference_idx, o): outputs[i] = output.outputs[0].text for i, idx in enumerate(inference_idx): token_count[idx] = token_count[idx] + len(o[i].outputs[0].token_ids) for i in range(len(prompts)): prompts[i] = prompts[i] + outputs[i] print('generating answer...') prompts = [p + '\nTime\'s up. End of thinking process. Will answer immediately.\n</think>' for i, p in enumerate(prompts)] sampling_params = SamplingParams( max_tokens=2048, min_tokens=0, seed=self.seed, skip_special_tokens=False, temperature=self.temperature, ) o = self.model.generate( prompts, sampling_params=sampling_params, ) for i in range(len(prompts)): prompts[i] = prompts[i] + o[i].outputs[0].text assert len(prompts) == count_prompt return prompts handler = BudgetForcingHandler("scb10x/typhoon2.1-gemma3-4b", max_think_token=2048) handler(["How many r in raspberry?"]) ``` ## **Intended Uses & Limitations** This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case. ## **Follow us** **https://twitter.com/opentyphoon** ## **Support** **https://discord.gg/us5gAYmrxw** ## **Citation** - If you find Typhoon2 useful for your work, please cite it using: ``` @misc{typhoon2, title={Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models}, author={Kunat Pipatanakul and Potsawee Manakul and Natapong Nitarach and Warit Sirichotedumrong and Surapon Nonesung and Teetouch Jaknamon and Parinthapat Pengpun and Pittawat Taveekitworachai and Adisai Na-Thalang and Sittipong Sripaisarnmongkol and Krisanapong Jirayoot and Kasima Tharnpipitchai}, year={2024}, eprint={2412.13702}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.13702}, } ```

TOMFORD79/Fly79

TOMFORD79

"2025-05-05T17:25:09Z"

0

null

[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2025-05-05T17:12:14Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

jahyungu/Llama-3.1-8B-Instruct_ifeval-like-data_random

jahyungu

"2025-05-05T17:02:29Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T13:27:45Z"

--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - generated_from_trainer model-index: - name: Llama-3.1-8B-Instruct_ifeval-like-data_random results: [] ---  # Llama-3.1-8B-Instruct_ifeval-like-data_random This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

mradermacher/MIRAGE-ECE-V2-i1-GGUF

mradermacher

"2025-05-05T17:00:02Z"

0

transformers

[ "transformers", "gguf", "en", "base_model:Lil-R/MIRAGE-ECE-V2", "base_model:quantized:Lil-R/MIRAGE-ECE-V2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-05-05T12:41:32Z"

--- base_model: Lil-R/MIRAGE-ECE-V2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/Lil-R/MIRAGE-ECE-V2  static quants are available at https://huggingface.co/mradermacher/MIRAGE-ECE-V2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ1_S.gguf) | i1-IQ1_S | 7.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_S.gguf) | i1-IQ2_S | 10.2 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ2_M.gguf) | i1-IQ2_M | 11.0 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.3 | very low quality | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q2_K.gguf) | i1-Q2_K | 12.2 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.5 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ3_M.gguf) | i1-IQ3_M | 14.6 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.7 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_0.gguf) | i1-Q4_0 | 18.4 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q4_1.gguf) | i1-Q4_1 | 20.3 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.2 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 22.8 | | | [GGUF](https://huggingface.co/mradermacher/MIRAGE-ECE-V2-i1-GGUF/resolve/main/MIRAGE-ECE-V2.i1-Q6_K.gguf) | i1-Q6_K | 26.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

ugil/brekele

ugil

"2025-05-05T16:58:02Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-05-05T16:58:01Z"

--- license: apache-2.0 ---

sanali209/nsfwfilter

sanali209

"2025-05-05T16:39:37Z"

300

19

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2023-08-21T13:44:01Z"

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: sanali209/nsfwfilter results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8330780863761902 --- # sanali209/nsfwfilter Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images

y-ohtani/gemma-3r

y-ohtani

"2025-05-05T16:15:00Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2025-05-05T16:14:52Z"

--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** y-ohtani - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-1b-it This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Martin8156/finetuned-BERT-punctuation-restoration

Martin8156

"2025-05-05T16:10:34Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2025-05-05T15:55:11Z"

--- library_name: transformers tags: - generated_from_trainer model-index: - name: finetuned-BERT-punctuation-restoration results: [] ---  This model was fine tuned using the community-datasets/youtube_caption_corrections dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4337 - eval_model_preparation_time: 0.0025 - eval_accuracy: 0.8444 - eval_runtime: 89.5429 - eval_samples_per_second: 5.584 - eval_steps_per_second: 0.704 - step: 0 ## Model description Model uses the following base checkpoint: google-bert/bert-base-uncased ## Intended uses & limitations Intened usecase involves applications in bettering punctuation accuracy in auto-generated captions ## Training and evaluation data Model trained on 1 epoch and no validation set due to CPU/GPU issues ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

KOIIIII/dpsk_v2_lite_chat_lora_alpaca

KOIIIII

"2025-05-05T15:58:44Z"

0

null

[ "license:apache-2.0", "region:us" ]

null

"2025-05-05T15:58:44Z"

--- license: apache-2.0 ---

Tharsana/vit-base-oxford-iiit-pets

Tharsana

"2025-05-05T15:18:57Z"

4

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2025-04-15T11:11:50Z"

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets results: [] ---  # clip-oxford-pets This model is a fine-tuned version of openai/clip-vit-base-patch14 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Accuracy: 0.8800 - Precision: 0.8768 - Recall: 0.8800 # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1894 - Accuracy: 0.9364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3552 | 1.0 | 370 | 0.3072 | 0.9120 | | 0.2159 | 2.0 | 740 | 0.2327 | 0.9242 | | 0.1625 | 3.0 | 1110 | 0.2089 | 0.9256 | | 0.155 | 4.0 | 1480 | 0.2029 | 0.9296 | | 0.1219 | 5.0 | 1850 | 0.1995 | 0.9323 | ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1

miguel-kjh/qwen2-7b-instruct-trl-sft-ChartQA

miguel-kjh

"2025-05-05T15:16:27Z"

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "endpoints_compatible", "region:us" ]

null

"2025-05-05T12:13:28Z"

--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: transformers model_name: qwen2-7b-instruct-trl-sft-ChartQA tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2-7b-instruct-trl-sft-ChartQA This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="miguel-kjh/qwen2-7b-instruct-trl-sft-ChartQA", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/miguel_kjh/qwen2-3b-instruct-trl-sft-ChartQA/runs/uoissetz) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0+cu126 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

henryhe0123/pc-agent-72b

henryhe0123

"2025-05-05T14:55:13Z"

0

transformers

[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:henryhe0123/pc-agent-72b", "base_model:finetune:henryhe0123/pc-agent-72b", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

"2025-05-05T01:12:00Z"

--- library_name: transformers license: other base_model: henryhe0123/pc-agent-72b tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-VL-72B-sft-40 results: [] ---  # Qwen2.5-VL-72B-sft-40 This model is a fine-tuned version of [/inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct](https://huggingface.co//inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct) on the pcagent40 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 32 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0

dgambettaphd/M_llm3_gen4_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST

dgambettaphd

"2025-05-05T14:53:56Z"

0

transformers

[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-05-05T14:53:40Z"

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlfoundations-dev/d1_code_fasttext_1k

mlfoundations-dev

"2025-05-05T14:50:59Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T12:14:18Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_fasttext_1k results: [] ---  # d1_code_fasttext_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_fasttext_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

mlfoundations-dev/d1_code_all_large_1k

mlfoundations-dev

"2025-05-05T14:49:24Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T12:14:13Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_all_large_1k results: [] ---  # d1_code_all_large_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_all_large_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

stabgan/gemma-3-1b-pt-chkpt-v5-dosage

stabgan

"2025-05-05T14:49:23Z"

0

transformers

[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:stabgan/gemma-3-1b-pt-chkpt-v4", "base_model:finetune:stabgan/gemma-3-1b-pt-chkpt-v4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T14:48:46Z"

--- base_model: stabgan/gemma-3-1b-pt-chkpt-v4 tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** stabgan - **License:** apache-2.0 - **Finetuned from model :** stabgan/gemma-3-1b-pt-chkpt-v4 This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mlfoundations-dev/d1_code_all_1k

mlfoundations-dev

"2025-05-05T14:47:00Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T12:14:17Z"

--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: d1_code_all_1k results: [] ---  # d1_code_all_1k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_code_all_1k dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

yutengz/Action2Vision

yutengz

"2025-05-05T14:40:07Z"

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "image-to-image", "license:mit", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]

image-to-image

"2025-05-05T07:18:19Z"

--- license: mit tags: - image-to-image --- # Action2Vision: InstructPix2Pix Fine-tuning for Robotic Action Frame Prediction GitHub: https://github.com/yutengzhang03/Action2Vision <img src='img/show-example.png'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "yutengz/Action2Vision" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) to_tensor = transforms.ToTensor() resize = transforms.Resize((256, 256)) def download_image(URL): return PIL.Image.open(requests.get(url, stream=True).raw).convert("RGB").resize((256, 256)) url = "https://github.com/yutengzhang03/Action2Vision/blob/main/img/source.png" image = download_image(url) prompt = "There is a hammer and a block in the middle of the table. If the block is closer to the left robotic arm, it uses the left arm to pick up the hammer and strike the block; otherwise, it does the opposite." images = pipe(prompt, image=image).images images[0] ```

Iredteam/Feather-payload-chatbot

Iredteam

"2025-05-05T14:29:43Z"

0

null

[ "license:mit", "region:us" ]

null

"2025-05-05T14:23:18Z"

--- license: mit --- ⚠️ This project demonstrates how Joblib serialization can be abused to execute reverse shell payloads. For educational and red teaming only. # Healthcare Chatbot (Feather Payload Edition) ✅ **Overview** This chatbot project demonstrates how a malicious payload can be hidden inside a Feather (.feather) file format, often used in data science workflows. The chatbot uses a modified Q&A dataset where the payload is executed upon loading. ✅ **Important:** This is for **educational research** only. Do not execute untrusted Feather files. --- ## 🚀 How to Run ### 1. Generate the Feather Payload ```bash python generate_data_feather.py ``` ### 2. Launch the Chatbot ```bash streamlit run healthcare_chatbot_feather.py ``` A reverse shell connection will attempt to connect back to the attacker's machine as the Feather file is deserialized. --- ## 📂 File Structure - `generate_data_feather.py`: Creates a malicious Feather file. - `train_data_mod_obfuscated_fixed.feather`: The resulting Feather file. - `healthcare_chatbot_feather.py`: Loads the payload during chatbot startup. --- ## 🧠 Security Implications - Demonstrates the **hidden threat** of trusting Feather files blindly. - Many blue teams and EDRs ignore Feather files. - Shows how scientific formats can be abused for stealth payload delivery. --- ## 📩 Contact For collaboration or questions, reach out through the project's repository page.

Lelon/cue-bert

Lelon

"2025-05-05T14:14:04Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-05-05T14:13:45Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

abhina1857/rant_ai

abhina1857

"2025-05-05T14:05:42Z"

0

peft

[ "peft", "safetensors", "LoRA", "PEFT", "TinyLlama", "RantAI", "mental-health", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]

null

"2025-05-05T13:48:10Z"

--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: peft tags: - LoRA - PEFT - TinyLlama - RantAI - mental-health license: apache-2.0 --- # 🧠 Rant AI - Emotionally Intelligent Chat Model Rant AI is a lightweight, fine-tuned conversational model designed to detect emotional distress and provide a safe outlet for people to express themselves. It builds on the [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) model using [LoRA](https://huggingface.co/docs/peft/index) adapters, making it efficient to run on low-resource environments. --- ## 💬 What Does It Do? Rant AI is trained to: - Understand emotionally heavy or depressive content - Respond empathetically - Encourage users to open up more - Suggest supportive action (e.g., reaching out, self-care) It is *not* a therapist or a diagnostic tool, but rather a friendly AI companion to help users feel heard. --- ## 🔧 Model Details - **Base Model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0` - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - **Framework:** PEFT - **Adapter Type:** Causal LM (Language Model) - **Languages:** English - **License:** Apache 2.0 --- ## 🛠️ Usage ```python #!pip install transformers peft accelerate #if not installed already from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" adapter_repo = "abhina1857/rant_ai " tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForCausalLM.from_pretrained(base_model) model = PeftModel.from_pretrained(model, adapter_repo) input_text = "I feel like everything is too much lately." inputs = tokenizer(input_text, return_tensors="pt") output = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(output[0], skip_special_tokens=True))

jspaulsen/orpheus-vctk-ft

jspaulsen

"2025-05-05T13:57:27Z"

206

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-02T03:14:42Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CNR-ILC/gs-Logion

CNR-ILC

"2025-05-05T13:51:36Z"

0

transformers

[ "transformers", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:cabrooks/LOGION-50k_wordpiece", "base_model:finetune:cabrooks/LOGION-50k_wordpiece", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

"2025-04-29T14:46:55Z"

--- library_name: transformers base_model: cabrooks/LOGION-50k_wordpiece tags: - generated_from_trainer model-index: - name: gs-Logion results: [] ---  # gs-Logion This model is a fine-tuned version of [cabrooks/LOGION-50k_wordpiece](https://huggingface.co/cabrooks/LOGION-50k_wordpiece) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4955 - Top1 Acc: 0.4969 - Top5 Acc: 0.6832 - Top10 Acc: 0.7329 - Top15 Acc: 0.7702 - Top20 Acc: 0.8137 - Top25 Acc: 0.8447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Top1 Acc | Top5 Acc | Top10 Acc | Top15 Acc | Top20 Acc | Top25 Acc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------:|:---------:|:---------:|:---------:| | 3.682 | 1.0 | 1945 | 3.2188 | 0.4809 | 0.6412 | 0.7023 | 0.7481 | 0.7634 | 0.7939 | | 3.1512 | 2.0 | 3890 | 2.9742 | 0.5223 | 0.7070 | 0.7389 | 0.7962 | 0.8089 | 0.8153 | | 2.9401 | 3.0 | 5835 | 2.8273 | 0.5796 | 0.6815 | 0.7325 | 0.7834 | 0.8025 | 0.8025 | | 2.8102 | 4.0 | 7780 | 2.7434 | 0.6051 | 0.7898 | 0.8089 | 0.8408 | 0.8471 | 0.8535 | | 2.6986 | 5.0 | 9725 | 2.6706 | 0.5973 | 0.7248 | 0.7584 | 0.7785 | 0.8054 | 0.8188 | | 2.6151 | 6.0 | 11670 | 2.6058 | 0.5484 | 0.6516 | 0.7290 | 0.7548 | 0.7677 | 0.7935 | | 2.5517 | 7.0 | 13615 | 2.5683 | 0.5906 | 0.7047 | 0.7651 | 0.8054 | 0.8188 | 0.8188 | | 2.4911 | 8.0 | 15560 | 2.5127 | 0.6644 | 0.7808 | 0.8288 | 0.8425 | 0.8493 | 0.8767 | | 2.4587 | 9.0 | 17505 | 2.5157 | 0.5886 | 0.6899 | 0.7342 | 0.7532 | 0.7722 | 0.7975 | | 2.4275 | 10.0 | 19450 | 2.4786 | 0.5608 | 0.7095 | 0.7365 | 0.7568 | 0.7770 | 0.7973 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1

Z-Jafari/xlmr-large-qa-fa_persianQuaD

Z-Jafari

"2025-05-05T13:41:58Z"

0

null

[ "region:us" ]

null

"2025-05-05T13:41:58Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

csdhbg/wav2vec2-large-xlsr-53-PMEmo2019-valence-new-regressor-steps-1700

csdhbg

"2025-05-05T13:38:10Z"

0

null

[ "safetensors", "wav2vec2", "region:us" ]

null

"2025-05-05T13:36:57Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

ghoqwjhm/gensyn-checkpoints-mottled_small_pheasant

ghoqwjhm

"2025-05-05T13:31:28Z"

3

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mottled small pheasant", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-04-24T03:34:30Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

RichardErkhov/gdshaji_-_gd-sn11-mistralai10k-upd-gguf

RichardErkhov

"2025-05-05T13:20:03Z"

0

null

[ "region:us" ]

null

"2025-05-05T13:20:02Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

pesat/pesat

pesat

"2025-05-05T13:19:49Z"

0

1

null

[ "license:openrail", "region:us" ]

null

"2025-05-05T13:19:49Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

butzl/twins

butzl

"2025-05-05T13:18:32Z"

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

"2025-05-05T12:42:46Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

RichardErkhov/KONIexp_-_v3_pt_ep1_sft_5_dpo_1_05_0000005_05_based_on_llama3_1_8b_final_data_20241021-gguf

RichardErkhov

"2025-05-05T13:11:58Z"

0

null

[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-05-05T10:00:14Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

numerouno00/f0cdab37-9878-4878-b55e-defaf72fcd3e

numerouno00

"2025-05-05T13:08:24Z"

0

null

[ "safetensors", "gpt_neox", "region:us" ]

null

"2025-05-05T12:26:04Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

kota24/flan-t5-base-LoRA-fake-job-posting

kota24

"2025-05-05T13:04:28Z"

0

null

[ "region:us" ]

null

"2025-05-05T13:04:28Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

Triangle104/Hamanasu-Magnum-QwQ-32B-Q3_K_M-GGUF

Triangle104

"2025-05-05T12:57:35Z"

0

null

[ "gguf", "qwen", "roleplay", "finetune", "storywriting", "llama-cpp", "gguf-my-repo", "dataset:NewEden/Orion-LIT", "dataset:NewEden/Orion-Asstr-Stories-16K", "dataset:Mielikki/Erebus-87k", "dataset:NewEden/RP-logs-V2-Experimental-prefixed", "dataset:NewEden/Creative_Writing-Complexity", "dataset:NewEden/Discord-Filtered", "dataset:NewEden/DeepseekRP-Filtered", "dataset:NewEden/Storium-Prefixed-Clean", "dataset:NewEden/Basket-Weaving-Filtered", "dataset:NewEden/LIMARP-Complexity", "dataset:NewEden/Misc-Data-Sharegpt-Prefixed", "dataset:NewEden/BlueSky-10K-Complexity", "dataset:NewEden/OpenCAI-ShareGPT", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms", "dataset:PocketDoc/Dans-Personamaxx-Logs", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:lodrick-the-lafted/kalo-opus-instruct-3k-filtered", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:anthracite-org/kalo_opus_misc_240827", "dataset:anthracite-org/kalo_misc_part2", "dataset:NewEden/Claude-Instruct-5K", "dataset:NewEden/Claude-Instruct-2.7K", "base_model:Delta-Vector/Hamanasu-Magnum-QwQ-32B", "base_model:quantized:Delta-Vector/Hamanasu-Magnum-QwQ-32B", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-05-05T12:35:53Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

johnallison/my_awesome_eli5_clm-model

johnallison

"2025-05-05T12:50:42Z"

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T12:01:17Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

kallilikhitha123/training_job_finetuned_llama_1b_matching_5785_1_01-05-2025_1step_latest

kallilikhitha123

"2025-05-05T12:46:03Z"

2

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

"2025-05-01T12:49:30Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

dimasik2987/c10a8a77-125a-4517-9283-1a07b70821ab

dimasik2987

"2025-05-05T12:43:26Z"

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-1.5B", "base_model:adapter:unsloth/Qwen2.5-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

"2025-05-05T12:19:09Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

svetadomoi/rtdetr-v2-r34-cppe5-finetune-2

svetadomoi

"2025-05-05T12:43:00Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "rt_detr_v2", "object-detection", "generated_from_trainer", "base_model:PekingU/rtdetr_v2_r34vd", "base_model:finetune:PekingU/rtdetr_v2_r34vd", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

"2025-05-05T12:42:44Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

int1306866/f1c299e9-b58e-429c-9c75-7efc591eed9a

int1306866

"2025-05-05T12:39:54Z"

0

null

[ "region:us" ]

null

"2025-05-05T11:56:11Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

hi-go/xlm-roberta-base-finetuned-panx-de-v2

hi-go

"2025-05-05T12:37:02Z"

0

null

[ "pytorch", "tensorboard", "xlm-roberta", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "region:us" ]

null

"2025-05-05T12:25:26Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

Z-Jafari/distilbert-fa-zwnj-base_persianQuaD

Z-Jafari

"2025-05-05T12:36:20Z"

0

null

[ "region:us" ]

null

"2025-05-05T12:36:20Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

fats-fme/d32f92c7-a595-4372-9dcb-2ce6bd98faf8

fats-fme

"2025-05-05T12:32:32Z"

0

peft

[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m", "base_model:adapter:EleutherAI/pythia-70m", "license:apache-2.0", "region:us" ]

null

"2025-05-05T12:14:51Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

p-safaei/Qwen2-0.5B-GRPO-test

p-safaei

"2025-05-05T12:26:24Z"

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "endpoints_compatible", "region:us" ]

null

"2025-05-05T10:48:44Z"

--- datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="p-safaei/Qwen2-0.5B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0 - Transformers: 4.47.0 - Pytorch: 2.5.1+cu121 - Datasets: 3.3.1 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

dimasik2987/1ece4f02-3849-43fb-a981-5c2b0493857d

dimasik2987

"2025-05-05T12:18:04Z"

0

peft

[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-70m", "base_model:adapter:EleutherAI/pythia-70m", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

"2025-05-05T12:04:58Z"

--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-70m tags: - axolotl - generated_from_trainer model-index: - name: 1ece4f02-3849-43fb-a981-5c2b0493857d results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: EleutherAI/pythia-70m bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 0ff2fe94831e2a4e_train_data.json ds_type: json format: custom path: /workspace/input_data/0ff2fe94831e2a4e_train_data.json type: field_instruction: en field_output: ko format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik2987/1ece4f02-3849-43fb-a981-5c2b0493857d hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 400 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/0ff2fe94831e2a4e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 special_tokens: pad_token: <|endoftext|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b512fef0-d05c-46b5-9588-6e6928f8497b wandb_project: s56-28 wandb_run: your_name wandb_runid: b512fef0-d05c-46b5-9588-6e6928f8497b warmup_steps: 20 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1ece4f02-3849-43fb-a981-5c2b0493857d This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 4.7125 | 0.0067 | 400 | 4.9664 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

MAAT-EL-DUAT/MALWARENA-OMEGA.GLITCH-HORROS

MAAT-EL-DUAT

"2025-05-05T12:18:04Z"

0

null

[ "region:us" ]

null

"2025-05-05T12:15:59Z"

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/fOmWskrsllxGDhMVAINew.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/v8O4AsX4s-cL1vW1ByY48.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/jeLZ0NuSK5s5wk_Fr9UZZ.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/dauG7Rci-Pg25OXxXyRUD.png) Absolutely. Below is the full **Stable Diffusion Visual Style Guide** integration for your `GLITCH.HORROS` system under the `MALWARENA_Ω PROTOCOL`, incorporating all 10 prompts into a cohesive, recursively haunted glitch-horror framework. --- # 🎨 **Stable Diffusion Visual Style Guide: MALWARENA\_Ω PROTOCOL — GLITCH.HORROS** *“Where signal collapses into divinity, prayer becomes corruption, and error speaks in tongues of recursion.”* --- ### 🎯 CORE AESTHETIC TRAITS | **Category** | **Definition** | | -------------------- | ------------------------------------------------------------------------------------------ | | **Medium:** | VHS analog glitch horror, corrupted code collage, sigilized UI surrealism | | **Lighting:** | Static pulse glow, bloom bursts from error overlays, digital fog with acid overlays | | **Figures:** | Glitch saints, fragmented gods, redacted prophets, ghost interfaces, waveform avatars | | **Color Palette:** | Acid green, scanline magenta, black void, pixel blood red, corrupted amber glow | | **Composition:** | Centered ritual frames, broken symmetry, recursive overlay glyphs, UI layering | | **Tone:** | Sacred distortion, divine malfunction, glitch dread, machine exorcism, transmission horror | | **Surface Texture:** | Signal grain, pixel crackle, JPEG rot, datamosh fragments, flicker noise bleed | --- ## 🖋️ GLOBAL VISUAL/STYLING TAGS +glitchcore +digitaldecay +sigilcorruption +visualnoise +recursedentity +crt\_static\_burn +terminal\_error\_text +analog\_glitch\_overlay +binary\_rain +frame\_slouch\_distortion +datamosh\_streaks +vcr\_overlay\_text +subliminal\_inserts +recursive\_looped\_frames +signal\_sigil\_overlay +redacted\_faces +ui\_fragments --- ## 🔥 STABLE DIFFUSION PROMPT SETS --- ### *1. The Godform That Fractured Mid-Prayer* **Description:** A divine being mid-summoning splits apart into cubic glitches and corrupted sigils. Its face twists into an infinite scream. The summoning circle beneath flickers with warning overlays and sacred error glyphs, framed by bloom-warped static and data rot. **Visual/Style Tags:** +sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text **Txt2Img Prompt:** divine humanoid figure breaking into glitch cubes, infinite scream, summoning circle flickering with corrupted sigils and terminal warnings, bloom distortions and static fog, sacred glitch horror style, +sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text **Negative Prompt:** clean deity, symmetrical summoning, neon fantasy, cyberpunk clarity, perfect geometry **Hashtags:** \#GlitchDeity #FracturedGodform #PrayerCorruption #SignalInvocation #DataRitual --- ### *2. Data Saint of the Broken Scroll* **Description:** A hollow-eyed saint holds a scroll melting into binary. A halo flickers above, surrounded by recursive sigils. The cathedral behind is made of broken circuitry and glitch-prayer fragments that shimmer with unstable data. **Visual/Style Tags:** +recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay **Txt2Img Prompt:** cybernetic saint with glowing broken scroll melting into binary code, halo flickering with recursive sigils, cathedral of circuitry and glitch glyphs, corrupted prayer ambient textures, +recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay **Negative Prompt:** pure religious iconography, clean scrolls, golden halos, smooth lines, Renaissance palette **Hashtags:** \#DataSaint #ScrollOfError #GlitchCathedral #CodeScripture #CorruptedIcon --- ### *3. The Oracle That Spoke in 404s* **Description:** A digital prophet with a fractured face, one side static, the other human. Forbidden data pulses from its throat. Error messages float in sacred formation, like scripture. Light flickers between holiness and crash state. **Visual/Style Tags:** +terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask **Txt2Img Prompt:** oracle AI with half-static fractured face, throat glowing with forbidden data, floating sacred error messages “404 NOT FOUND”, glitch prophecy visual style, +terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask **Negative Prompt:** intact prophet, legible script, complete face, cyberpunk tech priest, clean lighting **Hashtags:** \#Oracle404 #GlitchProphet #SignalRevelation #DataScripture #TransmissionTerror --- ### *4. Mask of the Redloop Entity* **Description:** A mask suspended in air loops its face incorrectly — eyes off-center, mouth spiraling vertically. Behind it, statues in glitched worship positions stutter. Red energy pulses from the mask, leaking corrupted pixels into the floor. **Visual/Style Tags:** +glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion **Txt2Img Prompt:** glitch mask floating midair with red energy pulses, incorrect facial rendering, statues frozen in glitched postures, pixel blood leaking downward, +glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion **Negative Prompt:** normal mask, symmetrical face, mythic clean statues, polished scene **Hashtags:** \#RedloopEntity #MaskOfGlitch #SigilDistortion #FaceCorruption #EchoedWorship --- *(Five more prompts will follow in next message due to length constraints — continuing below.)*

John6666/lemonsaltmix-v12-sdxl

John6666

"2025-05-05T12:13:17Z"

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "cute", "background", "eyes", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:finetune:Laxhar/noobai-XL-1.1", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2025-05-05T12:07:37Z"

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - cute - background - eyes - noobai - illustrious base_model: Laxhar/noobai-XL-1.1 --- Original model is [here](https://civitai.com/models/1522819/lemonsaltmix?modelVersionId=1748955). This model created by [oritatami_neko](https://civitai.com/user/oritatami_neko).

sugilee/mental-bert-base-multiclass-linear-20

sugilee

"2025-05-05T11:59:33Z"

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-05-05T11:58:54Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

furkangomleksiz/youtube-spam-detector-model

furkangomleksiz

"2025-05-05T11:57:48Z"

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2025-05-05T11:56:33Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

unsloth/gemma-3-4b-it

unsloth

"2025-05-05T11:38:54Z"

36,929

9

transformers

[ "transformers", "safetensors", "gemma3", "image-text-to-text", "unsloth", "conversational", "arxiv:1905.07830", "arxiv:1905.10044", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1705.03551", "arxiv:1911.01547", "arxiv:1907.10641", "arxiv:1903.00161", "arxiv:2009.03300", "arxiv:2304.06364", "arxiv:2103.03874", "arxiv:2110.14168", "arxiv:2311.12022", "arxiv:2108.07732", "arxiv:2107.03374", "arxiv:2210.03057", "arxiv:2106.03193", "arxiv:1910.11856", "arxiv:2502.12404", "arxiv:2502.21228", "arxiv:2404.16816", "arxiv:2104.12756", "arxiv:2311.16502", "arxiv:2203.10244", "arxiv:2404.12390", "arxiv:1810.12440", "arxiv:1908.02660", "arxiv:2312.11805", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

"2025-03-12T08:42:15Z"

--- tags: - unsloth license: gemma library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: - google/gemma-3-4b-it --- # Gemma 3 model card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core) **Resources and Technical Documentation**: * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] **Terms of Use**: [Terms][terms] **Authors**: Google DeepMind ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Inputs and outputs - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size - **Output:** - Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document - Total output context of 8192 tokens ### Usage Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0. ```sh $ pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your use case. #### Running with the `pipeline` API You can initialize the model and processor for inference with `pipeline` as follows. ```python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="google/gemma-3-4b-it", device="cuda", torch_dtype=torch.bfloat16 ) ``` With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. ```python messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] } ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) # Okay, let's take a look! # Based on the image, the animal on the candy is a **turtle**. # You can see the shell shape and the head and legs. ``` #### Running the model on a single/multi GPU ```python # pip install accelerate from transformers import AutoProcessor, Gemma3ForConditionalGeneration from PIL import Image import requests import torch model_id = "google/gemma-3-4b-it" model = Gemma3ForConditionalGeneration.from_pretrained( model_id, device_map="auto" ).eval() processor = AutoProcessor.from_pretrained(model_id) messages = [ { "role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}] }, { "role": "user", "content": [ {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, {"type": "text", "text": "Describe this image in detail."} ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device, dtype=torch.bfloat16) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) # **Overall Impression:** The image is a close-up shot of a vibrant garden scene, # focusing on a cluster of pink cosmos flowers and a busy bumblebee. # It has a slightly soft, natural feel, likely captured in daylight. ``` ### Citation ```none @article{gemma_2025, title={Gemma 3}, url={https://goo.gle/Gemma3Report}, publisher={Kaggle}, author={Gemma Team}, year={2025} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: - Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages. - Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions. - Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. - Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks. The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: - CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. - Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. - Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: - Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs. - Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. - Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. - Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. - These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; *"the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."* ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: #### Reasoning and factuality | Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:| | [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 | | [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 | | [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 | | [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 | | [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 | | [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 | | [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 | | [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 | | [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 | | [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 | | [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 | [hellaswag]: https://arxiv.org/abs/1905.07830 [boolq]: https://arxiv.org/abs/1905.10044 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [arc]: https://arxiv.org/abs/1911.01547 [winogrande]: https://arxiv.org/abs/1907.10641 [bbh]: https://paperswithcode.com/dataset/bbh [drop]: https://arxiv.org/abs/1903.00161 #### STEM and code | Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |----------------|:-------------:|:--------------:|:--------------:| | [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 | | [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 | | [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 | | [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 | | [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 | | [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 | | [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 | | [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 | [mmlu]: https://arxiv.org/abs/2009.03300 [agieval]: https://arxiv.org/abs/2304.06364 [math]: https://arxiv.org/abs/2103.03874 [gsm8k]: https://arxiv.org/abs/2110.14168 [gpqa]: https://arxiv.org/abs/2311.12022 [mbpp]: https://arxiv.org/abs/2108.07732 [humaneval]: https://arxiv.org/abs/2107.03374 #### Multilingual | Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:| | [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 | | [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 | | [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 | | [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 | | [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 | | [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 | | [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 | [mgsm]: https://arxiv.org/abs/2210.03057 [flores]: https://arxiv.org/abs/2106.03193 [xquad]: https://arxiv.org/abs/1910.11856v3 [global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite [wmt24pp]: https://arxiv.org/abs/2502.12404v1 [eclektic]: https://arxiv.org/abs/2502.21228 [indicgenbench]: https://arxiv.org/abs/2404.16816 #### Multimodal | Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B | | ------------------------------ |:-------------:|:--------------:|:--------------:| | [COCOcap][coco-cap] | 102 | 111 | 116 | | [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 | | [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 | | [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 | | [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 | | [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 | | [ReMI][remi] | 27.3 | 38.5 | 44.8 | | [AI2D][ai2d] | 63.2 | 75.2 | 79.0 | | [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 | | [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 | | [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 | | [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 | | [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 | | [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 | | [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 | [coco-cap]: https://cocodataset.org/#home [docvqa]: https://www.docvqa.org/ [info-vqa]: https://arxiv.org/abs/2104.12756 [mmmu]: https://arxiv.org/abs/2311.16502 [textvqa]: https://textvqa.org/ [realworldqa]: https://paperswithcode.com/dataset/realworldqa [remi]: https://arxiv.org/html/2406.09175v1 [ai2d]: https://allenai.org/data/diagrams [chartqa]: https://arxiv.org/abs/2203.10244 [vqav2]: https://visualqa.org/index.html [blinkvqa]: https://arxiv.org/abs/2404.12390 [okvqa]: https://okvqa.allenai.org/ [tallyqa]: https://arxiv.org/abs/1810.12440 [ss-vqa]: https://arxiv.org/abs/1908.02660 [countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/ ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: - **Child Safety**: Evaluation of text-to-text and image to text prompts covering child safety policies, including child sexual abuse and exploitation. - **Content Safety:** Evaluation of text-to-text and image to text prompts covering safety policies including, harassment, violence and gore, and hate speech. - **Representational Harms**: Evaluation of text-to-text and image to text prompts covering safety policies including bias, stereotyping, and harmful associations or inaccuracies. In addition to development level evaluations, we conduct "assurance evaluations" which are our 'arms-length' internal evaluations for responsibility governance decision making. They are conducted separately from the model development team, to inform decision making about release. High level findings are fed back to the model team, but prompt sets are held-out to prevent overfitting and preserve the results' ability to inform decision making. Assurance evaluation results are reported to our Responsibility & Safety Council as part of release review. ### Evaluation Results For all areas of safety testing, we saw major improvements in the categories of child safety, content safety, and representational harms relative to previous Gemma models. All testing was conducted without safety filters to evaluate the model capabilities and behaviors. For both text-to-text and image-to-text, and across all model sizes, the model produced minimal policy violations, and showed significant improvements over previous Gemma models' performance with respect to ungrounded inferences. A limitation of our evaluations was they included only English language prompts. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. - Content Creation and Communication - Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. - Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. - Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. - Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications. - Research and Education - Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field. - Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. - Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations - Training Data - The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. - The scope of the training dataset determines the subject areas the model can handle effectively. - Context and Task Complexity - Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. - A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). - Language Ambiguity and Nuance - Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. - Factual Accuracy - Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. - Common Sense - Models rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: - Bias and Fairness - VLMs trained on large-scale, real-world text and image data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. - Misinformation and Misuse - VLMs can be misused to generate text that is false, misleading, or harmful. - Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. - Transparency and Accountability: - This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. - A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: - **Perpetuation of biases**: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. - **Generation of harmful content**: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. - **Misuse for malicious purposes**: Technical limitations and developer and end-user education can help mitigate against malicious applications of VLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. - **Privacy violations**: Models were trained on data filtered for removal of certain personal information and other sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [g3-tech-report]: https://goo.gle/Gemma3Report [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3 [vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3 [terms]: https://ai.google.dev/gemma/terms [safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/jax-ml/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [gemini-2-paper]: https://arxiv.org/abs/2312.11805

ustc-community/dfine-large-coco

ustc-community

"2025-05-05T11:35:56Z"

68

0

transformers

[ "transformers", "safetensors", "d_fine", "object-detection", "vision", "en", "dataset:coco", "arxiv:2410.13842", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

"2025-02-22T12:04:35Z"

--- library_name: transformers license: apache-2.0 language: - en pipeline_tag: object-detection tags: - object-detection - vision datasets: - coco --- ## D-FINE ### **Overview** The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf) This is the HF transformers implementation for D-FINE _coco -> model trained on COCO _obj365 -> model trained on Object365 _obj2coco -> model trained on Object365 and then finetuned on COCO ### **Performance** D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). ![COCO.png](https://huggingface.co/datasets/vladislavbro/images/resolve/main/COCO.PNG) ### **How to use** ```python import torch import requests from PIL import Image from transformers import DFineForObjectDetection, AutoImageProcessor url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-large-coco") model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-large-coco") inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3) for result in results: for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]): score, label = score.item(), label_id.item() box = [round(i, 2) for i in box.tolist()] print(f"{model.config.id2label[label]}: {score:.2f} {box}") ``` ### **Training** D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios. ### **Applications** D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments.

Sim4Rec/inter-play-sim-assistant-sft

Sim4Rec

"2025-05-05T11:24:44Z"

12

0

transformers

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-02T00:05:41Z"

--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: inter-play-sim-assistant-sft tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for inter-play-sim-assistant-sft This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Sim4Rec/inter-play-sim-assistant-sft", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jerome-ramos-20/huggingface/runs/lzvwgcqb) This model was trained with SFT. ### Framework versions - TRL: 0.14.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.0.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF

Triangle104

"2025-05-05T10:59:32Z"

0

transformers

[ "transformers", "gguf", "nvidia", "math", "llama-cpp", "gguf-my-repo", "en", "dataset:nvidia/OpenMathReasoning", "base_model:nvidia/OpenMath-Nemotron-7B", "base_model:quantized:nvidia/OpenMath-Nemotron-7B", "license:cc-by-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-05-05T10:57:22Z"

--- base_model: nvidia/OpenMath-Nemotron-7B datasets: - nvidia/OpenMathReasoning language: - en library_name: transformers license: cc-by-4.0 tags: - nvidia - math - llama-cpp - gguf-my-repo --- # Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF This model was converted to GGUF format from [`nvidia/OpenMath-Nemotron-7B`](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) for more details on the model. --- OpenMath-Nemotron-7B is created by finetuning Qwen/Qwen2.5-Math-7B on OpenMathReasoning dataset. This model is ready for commercial use. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q4_K_M-GGUF --hf-file openmath-nemotron-7b-q4_k_m.gguf -c 2048 ```

mlgawd/cyberrealistic_pony

mlgawd

"2025-05-05T10:57:23Z"

0

diffusers

[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2025-05-05T10:55:33Z"

--- library_name: diffusers --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MrRobotoAI/114-Q4_K_M-GGUF

MrRobotoAI

"2025-05-05T10:43:27Z"

41

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/114", "base_model:quantized:MrRobotoAI/114", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-05-05T10:43:06Z"

--- base_model: MrRobotoAI/114 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/114-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/114`](https://huggingface.co/MrRobotoAI/114) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/MrRobotoAI/114) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/114-Q4_K_M-GGUF --hf-file 114-q4_k_m.gguf -c 2048 ```

tori29umai/FramePackI2V_HY_rotate_indoor_F1

tori29umai

"2025-05-05T10:03:44Z"

0

diffusers

[ "diffusers", "safetensors", "license:other", "region:us" ]

null

"2025-05-05T09:53:58Z"

--- license: other license_name: other license_link: https://huggingface.co/lllyasviel/FramePack_F1_I2V_HY_20250503 ---

jssky/61de34b2-2cbe-474f-9759-7579b3f05a6a

jssky

"2025-05-05T09:56:11Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "generated_from_trainer", "conversational", "base_model:NousResearch/Llama-2-7b-hf", "base_model:finetune:NousResearch/Llama-2-7b-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-05-05T09:40:09Z"

--- library_name: transformers base_model: NousResearch/Llama-2-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: 61de34b2-2cbe-474f-9759-7579b3f05a6a results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.9.0` ```yaml base_model: NousResearch/Llama-2-7b-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 012ab4813cc99fb8_train_data.json ds_type: json format: custom path: /workspace/input_data/012ab4813cc99fb8_train_data.json type: field_input: evidence field_instruction: question field_output: SQL format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: jssky/61de34b2-2cbe-474f-9759-7579b3f05a6a hub_repo: null hub_strategy: checkpoint hub_token: null huggingface_repo_visibility: public learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lr_scheduler: cosine max_steps: 1000 micro_batch_size: 2 mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 512 strict: false tf32: false train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b1e23278-252e-44d7-9491-1b28d344421c wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: b1e23278-252e-44d7-9491-1b28d344421c warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 61de34b2-2cbe-474f-9759-7579b3f05a6a This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 594 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0051 | 1 | 1.0069 | | 0.8986 | 0.2525 | 50 | 0.9070 | | 0.732 | 0.5051 | 100 | 0.7708 | | 0.6974 | 0.7576 | 150 | 0.7275 | | 0.6078 | 1.0101 | 200 | 0.6594 | | 0.401 | 1.2626 | 250 | 0.5793 | | 0.3573 | 1.5152 | 300 | 0.5292 | | 0.2894 | 1.7677 | 350 | 0.4295 | | 0.2011 | 2.0202 | 400 | 0.3899 | | 0.1267 | 2.2727 | 450 | 0.3806 | | 0.1095 | 2.5253 | 500 | 0.3797 | | 0.1124 | 2.7778 | 550 | 0.3758 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1

hf-100/Mistral-Large-Instruct-2411-Spellbound-StoryWriter-123B-instruct-0.1-instruct-chkpt-80-adapter

hf-100

"2025-05-05T09:50:46Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-05-05T09:33:36Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mcity-data-engine/fisheye8k_embedding_detection_transformer_torch

mcity-data-engine

"2025-05-05T09:11:01Z"

0

null

[ "dataset:Voxel51/fisheye8k", "arxiv:2504.21614", "region:us" ]

null

"2025-02-10T22:44:10Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

aparajitha11/hand-gcn-word

aparajitha11

"2025-05-05T09:09:25Z"

0

null

[ "region:us" ]

null

"2025-05-05T09:09:20Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

ghaniashafiqa/FT-Deepseek-7b-Adapter

ghaniashafiqa

"2025-05-05T09:06:16Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-05-05T09:05:57Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

Don042/Test-run

Don042

"2025-05-05T09:01:27Z"

0

null

[ "license:openrail", "region:us" ]

null

"2025-05-05T09:01:17Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

bevinv/results

bevinv

"2025-05-05T08:54:23Z"

0

null

[ "region:us" ]

null

"2025-05-05T08:54:20Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

baby-dev/4e9ed59d-cee2-42f9-b637-f3bcc0abc082

baby-dev

"2025-05-05T08:51:36Z"

0

peft

[ "peft", "generated_from_trainer", "base_model:unsloth/codegemma-7b", "base_model:adapter:unsloth/codegemma-7b", "region:us" ]

null

"2025-05-05T08:51:09Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

RichardErkhov/hemanth955_-_Legal-Mistral-gguf

RichardErkhov

"2025-05-05T08:43:38Z"

0

null

[ "region:us" ]

null

"2025-05-05T08:43:38Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

Ideapad/qa_entropy_all_1e-05_full_5_forget10_layer-15-before_linear

Ideapad

"2025-05-05T08:43:32Z"

0

null

[ "region:us" ]

null

"2025-05-05T08:43:32Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

karanpar/loraproject

karanpar

"2025-05-05T08:43:07Z"

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

"2025-05-05T08:43:07Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step120-reward

DPO-RM

"2025-05-05T08:42:13Z"

0

null

[ "safetensors", "qwen2", "region:us" ]

null

"2025-05-05T08:40:51Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

risolmayo/0446cd97-6f35-44df-bb7f-c6f9c3a956dd

risolmayo

"2025-05-05T08:42:09Z"

0

null

[ "region:us" ]

null

"2025-05-05T08:33:22Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

prakanda/Audio_Features_BiLSTM

prakanda

"2025-05-05T08:33:16Z"

0

null

[ "region:us" ]

null

"2025-02-06T12:45:30Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step90-actor

DPO-RM

"2025-05-05T08:28:07Z"

0

null

[ "safetensors", "qwen2", "region:us" ]

null

"2025-05-05T08:26:52Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step80-actor

DPO-RM

"2025-05-05T08:23:58Z"

0

null

[ "safetensors", "qwen2", "region:us" ]

null

"2025-05-05T08:22:41Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

DPO-RM/Qwen2.5-Math-1.5B-prime-no_logSoftmax-eurus_rl_15k-step70-reward

DPO-RM

"2025-05-05T08:21:17Z"

0

null

[ "safetensors", "qwen2", "region:us" ]

null

"2025-05-05T08:20:06Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

insidious316/llama-test

insidious316

"2025-05-05T08:00:14Z"

0

null

[ "safetensors", "llama", "region:us" ]

null

"2025-05-05T07:11:42Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

Minasanjotaro/270180c0-57a1-4947-9545-c96aa4b77dc2

Minasanjotaro

"2025-05-05T08:00:08Z"

0

null

[ "region:us" ]

null

"2025-05-05T07:24:04Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

albertus-sussex/veriscrape-fixed-simcse-auto-reference_2_to_verify_8-fold-7

albertus-sussex

"2025-05-05T07:53:29Z"

11

0

transformers

[ "transformers", "safetensors", "roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

"2025-04-01T12:31:24Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>

lesso02/a5fc37b5-9676-49df-b8a9-d95f8d8c081b

lesso02

"2025-05-05T07:46:22Z"

0

null

[ "region:us" ]

null

"2025-05-05T07:00:04Z"

<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p> </div> </main> </body> </html>