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stanpony/tinylm33M-stella-1sent_32clust-2025-04-05-01-08_full | stanpony | "2025-04-05T01:37:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T01:37:42Z" | ---
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. This model card has been automatically generated.
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mihira04/model | mihira04 | "2025-04-05T01:37:00Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T01:29:12Z" | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mihira04
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama 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)
|
glif-loradex-trainer/Hailey_LostVHS | glif-loradex-trainer | "2025-04-05T01:32:49Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] | text-to-image | "2025-04-05T01:32:35Z" | ---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1743816687414__000000500_0.jpg
text: VHS Cover for a movie about a wounded centaur, mythical creature VHS
- output:
url: samples/1743816711888__000000500_1.jpg
text: VHS cover for documentary about ruins of athens VHS
- output:
url: samples/1743816737203__000000500_2.jpg
text: Legend of the silver vampire sword VHS
base_model: black-forest-labs/FLUX.1-dev
trigger: "VHS"
instance_prompt: "VHS"
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
---
# LostVHS
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Hailey`.
<Gallery />
## Trigger words
You should use `VHS` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/Hailey_LostVHS/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
KASHU101/mistral-paper-summarizer | KASHU101 | "2025-04-05T01:32:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"summarization",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | summarization | "2025-04-04T19:13:48Z" | ---
library_name: transformers
pipeline_tag: summarization
---
# 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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[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]
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<!-- 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]
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#### 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]
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[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]
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- **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]
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## Glossary [optional]
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[More Information Needed]
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## Model Card Contact
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yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-deepseek-6k | yeok | "2025-04-05T01:32:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T23:42:42Z" | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: qwen-2.5-1.5B-instruct-sft-lora-countdown-deepseek-6k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen-2.5-1.5B-instruct-sft-lora-countdown-deepseek-6k
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="yeok/qwen-2.5-1.5B-instruct-sft-lora-countdown-deepseek-6k", 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/yeokch/stream-of-search-train/runs/9x3udzje)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RichardErkhov/fumanji420_-_test-gguf | RichardErkhov | "2025-04-05T01:28:03Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-05T00:07:06Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
test - GGUF
- Model creator: https://huggingface.co/fumanji420/
- Original model: https://huggingface.co/fumanji420/test/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [test.Q2_K.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q2_K.gguf) | Q2_K | 1.27GB |
| [test.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [test.IQ3_S.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [test.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [test.IQ3_M.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [test.Q3_K.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q3_K.gguf) | Q3_K | 1.57GB |
| [test.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [test.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [test.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [test.Q4_0.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q4_0.gguf) | Q4_0 | 1.79GB |
| [test.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [test.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [test.Q4_K.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q4_K.gguf) | Q4_K | 1.88GB |
| [test.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [test.Q4_1.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q4_1.gguf) | Q4_1 | 1.95GB |
| [test.Q5_0.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q5_0.gguf) | Q5_0 | 2.11GB |
| [test.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [test.Q5_K.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q5_K.gguf) | Q5_K | 2.16GB |
| [test.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [test.Q5_1.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q5_1.gguf) | Q5_1 | 2.28GB |
| [test.Q6_K.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q6_K.gguf) | Q6_K | 2.46GB |
| [test.Q8_0.gguf](https://huggingface.co/RichardErkhov/fumanji420_-_test-gguf/blob/main/test.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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]
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- **License:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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### Direct Use
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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<!-- 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]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### 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]
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[More Information Needed]
|
zera09/smol-dpo_v1 | zera09 | "2025-04-05T01:23:47Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T01:23:39Z" | ---
base_model: HuggingFaceTB/SmolVLM-Instruct
library_name: transformers
model_name: smol-dpo_v1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for smol-dpo_v1
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-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="zera09/smol-dpo_v1", 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/zeramarveenlyngkhoi/huggingface/runs/k33n2xoe)
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: 4.50.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- 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}}
}
``` |
silviasapora/mistral-7b-sft-dpo-basic-5e-7-005-v132 | silviasapora | "2025-04-05T01:19:14Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T19:40:34Z" | ---
library_name: transformers
model_name: mistral-7b-sft-dpo-basic-5e-7-005-v132
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for mistral-7b-sft-dpo-basic-5e-7-005-v132
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/mistral-7b-sft-dpo-basic-5e-7-005-v132", 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/5s66az46)
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: 0.16.1
- Transformers: 4.50.3
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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}}
}
``` |
Billyyy/mn_nllb_1.3B_continue | Billyyy | "2025-04-05T01:18:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/nllb-200-distilled-1.3B",
"base_model:adapter:facebook/nllb-200-distilled-1.3B",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2025-04-05T01:05:26Z" | ---
library_name: peft
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-1.3B
tags:
- generated_from_trainer
model-index:
- name: mn_nllb_1.3B_continue
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. -->
# mn_nllb_1.3B_continue
This model is a fine-tuned version of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.1680
## 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: 40
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 160
- 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_steps: 10
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0421 | 0.32 | 20 | 7.1684 |
| 7.0297 | 0.64 | 40 | 7.1668 |
| 7.0254 | 0.96 | 60 | 7.1666 |
| 7.0207 | 1.272 | 80 | 7.1669 |
| 7.0429 | 1.592 | 100 | 7.1672 |
| 7.0276 | 1.912 | 120 | 7.1675 |
| 7.0199 | 2.224 | 140 | 7.1675 |
| 7.0254 | 2.544 | 160 | 7.1678 |
| 7.0379 | 2.864 | 180 | 7.1678 |
| 7.0454 | 3.176 | 200 | 7.1680 |
| 7.0415 | 3.496 | 220 | 7.1680 |
| 7.0466 | 3.816 | 240 | 7.1680 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0 |
baninazar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_prickly_dove | baninazar | "2025-04-05T01:17:45Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am eager prickly dove",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T09:08:01Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_prickly_dove
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am eager prickly dove
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_prickly_dove
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="baninazar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-eager_prickly_dove", 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: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
EdMarcavage/DeepSeek-R1-Distill-Llama-8B | EdMarcavage | "2025-04-05T01:17:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T01:13:23Z" | ---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EdMarcavage
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama 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)
|
lesso16/753e32a2-f725-4dfd-886d-8cf184645a6f | lesso16 | "2025-04-05T01:12:12Z" | 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",
"region:us"
] | null | "2025-04-04T15:59:46Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 753e32a2-f725-4dfd-886d-8cf184645a6f
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
adapter: lora
base_model: unsloth/Qwen2.5-1.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a362c6305563432e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a362c6305563432e_train_data.json
type:
field_input: document_extracted
field_instruction: question
field_output: long_answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso16/753e32a2-f725-4dfd-886d-8cf184645a6f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000216
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4000
micro_batch_size: 4
mlflow_experiment_name: /tmp/a362c6305563432e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 160
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d777c939-6fd1-4c34-855a-6a705ef25e85
wandb_project: 16a
wandb_run: your_name
wandb_runid: d777c939-6fd1-4c34-855a-6a705ef25e85
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 753e32a2-f725-4dfd-886d-8cf184645a6f
This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1047
## 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: 0.000216
- train_batch_size: 4
- eval_batch_size: 4
- seed: 160
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0006 | 1 | 3.2512 |
| 0.1522 | 0.3186 | 500 | 0.2667 |
| 0.1706 | 0.6371 | 1000 | 0.2793 |
| 0.114 | 0.9557 | 1500 | 0.1632 |
| 0.0686 | 1.2743 | 2000 | 0.0951 |
| 0.0744 | 1.5929 | 2500 | 0.0935 |
| 0.0782 | 1.9114 | 3000 | 0.0922 |
| 0.0417 | 2.2300 | 3500 | 0.1033 |
| 0.0401 | 2.5486 | 4000 | 0.1047 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
kreasof-ai/distil-large-v3-en2hi | kreasof-ai | "2025-04-05T01:06:47Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:distil-whisper/distil-large-v3",
"base_model:finetune:distil-whisper/distil-large-v3",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-04-04T14:27:25Z" | ---
library_name: transformers
license: mit
base_model: distil-whisper/distil-large-v3
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: distil-large-v3-en2hi
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. -->
# distil-large-v3-en2hi
This model is a fine-tuned version of [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1126
- Bleu: 3.87
- Chrf: 15.79
- Wer: 97.5158
## 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: 4
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Chrf | Wer |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-----:|:-------:|
| 0.0199 | 0.2 | 1000 | 0.1302 | 0.78 | 11.55 | 95.0960 |
| 0.0223 | 0.4 | 2000 | 0.1224 | 2.19 | 14.14 | 94.4950 |
| 0.0461 | 0.6 | 3000 | 0.1168 | 1.83 | 13.03 | 95.5029 |
| 0.0316 | 0.8 | 4000 | 0.1141 | 3.67 | 15.82 | 96.2201 |
| 0.029 | 1.0 | 5000 | 0.1126 | 3.87 | 15.79 | 97.5158 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.0
|
myun11/xlm-roberta-base-finetuned-panx-de-fr | myun11 | "2025-04-05T01:06:01Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2025-04-02T16:47:54Z" | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
pipeline_tag: token-classification
widget:
- type: text-classification
text: "Jeff Dean ist ein Informatiker bei Google in Kalifornien"
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1624
- F1: 0.8538
## 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: 24
- eval_batch_size: 24
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3086 | 1.0 | 715 | 0.1770 | 0.8239 |
| 0.1621 | 2.0 | 1430 | 0.1650 | 0.8442 |
| 0.1221 | 3.0 | 2145 | 0.1624 | 0.8538 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
jwengr/gemma2-2b-kor-deobfuscation | jwengr | "2025-04-05T01:04:30Z" | 17 | 0 | null | [
"safetensors",
"hangul_gemma_deobfuscator",
"text2text-generation",
"custom_code",
"ko",
"base_model:google/gemma-2-2b",
"base_model:finetune:google/gemma-2-2b",
"region:us"
] | text2text-generation | "2025-03-21T07:24:41Z" | ---
language:
- ko
base_model:
- google/gemma-2-2b
pipeline_tag: text2text-generation
---
# 🧠 Obfuscated Korean Text Restoration
This repository is designed for restoring obfuscated Korean text.
It was developed and validated using the dataset from the 2024 Dacon Obfuscated Korean Review Restoration AI Competition
For more details on the dataset and modeling approach, please refer to the [**2024 Dacon Obfuscated Korean Review Restoration AI Competition**](https://dacon.io/en/competitions/official/236446/codeshare/12228?page=1&dtype=recent).
## 🔧 Features
This repository includes the following components:
1. **Pretrained Korean Text Restoration Model**
GemmaModel trained to restore obfuscated Korean text to its original, human-readable form.
2. **Syllable-level Korean Tokenizer**
A tokenizer tailored to process Korean at the syllable level for improved granularity and performance.
3. **Flexible Korean Sentence Splitter**
A sentence segmentation tool that handles the complexities of Korean syntax effectively.
4. **Korean Text Obfuscator**
A module for simulating text obfuscation, useful for training and evaluation.
### 1. **Pretrained Korean Text Restoration Model**
This pretrained model restores obfuscated Korean text by converting broken or scrambled Hangul into fluent, natural Korean.
Finetuned for Korean tour review restoration
#### ✅ Example Usage
##### For Short Text
```python
from transformers import AutoModel
# Load the tokenizer and model
hangul_tokenizer = AutoModel.from_pretrained('jwengr/gemma2-2b-kor-deobfuscation', subfolder='hangul_tokenizer', trust_remote_code=True)
hangul_deobfuscator = AutoModel.from_pretrained('jwengr/gemma2-2b-kor-deobfuscation', trust_remote_code=True)
hangul_deobfuscator.load_hangul_tokenizer(hangul_tokenizer)
# Example
text = '얀녕핥셈욧.'
restored = hangul_deobfuscator.deobfuscate(text)
print(restored) # '안녕하세요.'
```
##### For Long Sentences
```python
from transformers import AutoModel
# Load models
hangul_tokenizer = AutoModel.from_pretrained('jwengr/gemma2-2b-kor-deobfuscation', subfolder='hangul_tokenizer', trust_remote_code=True)
sentence_tokenizer = AutoModel.from_pretrained('jwengr/gemma2-2b-kor-deobfuscation', subfolder='sentence_tokenizer', trust_remote_code=True)
hangul_deobfuscator = AutoModel.from_pretrained('jwengr/gemma2-2b-kor-deobfuscation', trust_remote_code=True)
hangul_deobfuscator.load_hangul_tokenizer(hangul_tokenizer)
# Example
sentence = '''별 한 게토 았깝땀. 왜 싸람듯릭 펼 1캐를 쥰눈징 컥꺾폰 싸람믐롯섞 맒록 섧멍핥쟈닐 탯끎룐눈 녀뮤 퀼교... 야뭍툰 둠 변 닺씨 깍낄 싫훈 굣. 깸삥읊 20여 년 댜녁뵨 곧 중 쩨윌 귑푼 낙팠떤 곶.'''
restored = hangul_deobfuscator.deobfuscate(sentence, sentence_tokenizer)
print(restored)
# '별 한 개도 아깝다. 왜 사람들이 별 1개를 주는지 겪어본 사람으로서 말로 설명하자니 댓글로는 너무 길고... 아무튼 두 번 다시 가길 싫은 곳. 캠핑을 20여 년 다녀본 곳 중 제일 기분 나빴던 곳.'
```
### 2. **Syllable-level Korean Tokenizer**
A tokenizer tailored to process Korean at the syllable level for improved granularity and performance.
#### ✅ Example Usage
```python
from transformers import AutoModel
hangul_tokenizer = AutoModel.from_pretrained(
'jwengr/gemma2-2b-kor-deobfuscation',
subfolder='hangul_tokenizer',
trust_remote_code=True
)
encoded_ids, token_type_ids = hangul_tokenizer.encode_char('a안b녕c하d세e요!')
decoded_text = hangul_tokenizer.decode_char(encoded_ids, token_type_ids)
encoded_ids, token_type_ids = hangul_tokenizer.encode_jamo('a안b녕c하d세e요!')
decoded_text = hangul_tokenizer.decode_jamo(encoded_ids, token_type_ids)
print(decoded_text)
# Output: 'a안b녕c하d세e요!'
```
### 3. **Flexible Korean Sentence Splitter**
A sentence segmentation tool that handles the complexities of Korean syntax effectively.
#### ✅ Example Usage
```python
from transformers import AutoModel
sentence_tokenizer = AutoModel.from_pretrained(
'jwengr/gemma2-2b-kor-deobfuscation',
subfolder='sentence_tokenizer',
trust_remote_code=True
)
text = '''아... 가격 좋고 뷰도 뻥 뚫려서 시원하지만 담배 냄새 미쳐버림. 싸게 하루만 묵겠다! 하는 사람한테만 추천. 담배 냄새가 모든 장점을 가져가는 곳. 노래방에서 각종 담배와 유흥에 쩔었을 때 나는 냄새가 계속 방에 있음 ㅆ... 싸니까 할 말 없음.'''
# 문장 분리
chunks = sentence_tokenizer.split_text(text)
print(chunks)
# Output: [
# '아... 가격 좋고 뷰도 뻥 뚫려서 시원하지만 담배 냄새 미쳐버림. 싸게 하루만 묵겠다! 하는 사람한테만 추천. ',
# '담배 냄새가 모든 장점을 가져가는 곳. 노래방에서 각종 담배와 유흥에 쩔었을 때 나는 냄새가 계속 방에 있음 ',
# 'ㅆ... 싸니까 할 말 없음.'
# ]
# 오버랩 적용
chunks_overlapped = sentence_tokenizer.overlap(chunks)
print(chunks_overlapped)
# Output:
# [
# (0, 64, '아... 가격 좋고 뷰도 뻥 뚫려서 시원하지만 담배 냄새 미쳐버림. 싸게 하루만 묵겠다! 하는 사람한테만 추천.'),
# (17, 86, '뚫려서 시원하지만 담배 냄새 미쳐버림. 싸게 하루만 묵겠다! 하는 사람한테만 추천. 담배 냄새가 모든 장점을 가져가는 곳.'),
# (42, 109, '하루만 묵겠다! 하는 사람한테만 추천. 담배 냄새가 모든 장점을 가져가는 곳. 노래방에서 각종 담배와 유흥에 쩔었을 때'),
# (64, 125, '담배 냄새가 모든 장점을 가져가는 곳. 노래방에서 각종 담배와 유흥에 쩔었을 때 나는 냄새가 계속 방에 있음'),
# (86, 130, '노래방에서 각종 담배와 유흥에 쩔었을 때 나는 냄새가 계속 방에 있음 ㅆ...'),
# (109, 134, '나는 냄새가 계속 방에 있음 ㅆ... 싸니까'),
# (125, 141, 'ㅆ... 싸니까 할 말 없음.')
# ]
# 복원된 텍스트 출력
decoded = sentence_tokenizer.decode_overlap(chunks_overlapped)
print(decoded)
# Output:
# '아... 가격 좋고 뷰도 뻥 뚫려서 시원하지만 담배 냄새 미쳐버림. 싸게 하루만 묵겠다! 하는 사람한테만 추천. 담배 냄새가 모든 장점을 가져가는 곳. 노래방에서 각종 담배와 유흥에 쩔었을 때 나는 냄새가 계속 방에 있음 ㅆ... 싸니까 할 말 없음.'
```
### 4. **Korean Text Obfuscator**
A module for simulating Korean text obfuscation, useful for training, data augmentation, and evaluation.
It generates noisy or obfuscated versions of input text to mimic real-world corrupted or user-modified input.
#### ✅ Example Usage
```python
from transformers import AutoModel
hangul_augmentator = AutoModel.from_pretrained(
'jwengr/gemma2-2b-kor-deobfuscation',
subfolder='hangul_augmentator',
trust_remote_code=True
)
# 입력 문장
text = '안녕하세요'
# 난독화된 출력
obfuscated = hangul_augmentator(text)
print(obfuscated)
# Output: '안녕함쒷오'
``` |
barca-boy/grpo_lora_model_llama3.2-3b-it_full | barca-boy | "2025-04-05T01:04:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T01:03:55Z" | ---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** barca-boy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama 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)
|
cst7/robot_toy_flux_lora_500_style | cst7 | "2025-04-05T01:03:30Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-05T00:49:20Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks robot toy
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - cst7/robot_toy_flux_lora_500_style
<Gallery />
## Model description
These are cst7/robot_toy_flux_lora_500_style DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? True.
## Trigger words
You should use `a photo of sks robot toy` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/robot_toy_flux_lora_500_style/tree/main) in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('cst7/robot_toy_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks robot toy').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF | mradermacher | "2025-04-05T01:00:09Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Yobenboben/Qwen2.5-32B-Snowgnome-EXP",
"base_model:quantized:Yobenboben/Qwen2.5-32B-Snowgnome-EXP",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2025-04-04T18:51:15Z" | ---
base_model: Yobenboben/Qwen2.5-32B-Snowgnome-EXP
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Yobenboben/Qwen2.5-32B-Snowgnome-EXP
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-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/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-Snowgnome-EXP-i1-GGUF/resolve/main/Qwen2.5-32B-Snowgnome-EXP.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
MinaMila/phi3_unlearned_Adult_15ep_33 | MinaMila | "2025-04-05T00:54:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:MinaMila/Phi3_unlearning_general_methode",
"base_model:finetune:MinaMila/Phi3_unlearning_general_methode",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:51:57Z" | ---
base_model: MinaMila/Phi3_unlearning_general_methode
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MinaMila
- **License:** apache-2.0
- **Finetuned from model :** MinaMila/Phi3_unlearning_general_methode
This llama 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)
|
RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf | RichardErkhov | "2025-04-05T00:53:42Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-05T00:16:29Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3.2-3B-insights - GGUF
- Model creator: https://huggingface.co/noaebbot/
- Original model: https://huggingface.co/noaebbot/llama3.2-3B-insights/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama3.2-3B-insights.Q2_K.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama3.2-3B-insights.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama3.2-3B-insights.IQ3_S.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama3.2-3B-insights.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama3.2-3B-insights.IQ3_M.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama3.2-3B-insights.Q3_K.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama3.2-3B-insights.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama3.2-3B-insights.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama3.2-3B-insights.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama3.2-3B-insights.Q4_0.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama3.2-3B-insights.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama3.2-3B-insights.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama3.2-3B-insights.Q4_K.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama3.2-3B-insights.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama3.2-3B-insights.Q4_1.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama3.2-3B-insights.Q5_0.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama3.2-3B-insights.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama3.2-3B-insights.Q5_K.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama3.2-3B-insights.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama3.2-3B-insights.Q5_1.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama3.2-3B-insights.Q6_K.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama3.2-3B-insights.Q8_0.gguf](https://huggingface.co/RichardErkhov/noaebbot_-_llama3.2-3B-insights-gguf/blob/main/llama3.2-3B-insights.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** noaebbot
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama 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)
|
cst7/remote_control_car_flux_lora_500_style | cst7 | "2025-04-05T00:49:06Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-05T00:34:57Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks remote control car
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - cst7/remote_control_car_flux_lora_500_style
<Gallery />
## Model description
These are cst7/remote_control_car_flux_lora_500_style DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? True.
## Trigger words
You should use `a photo of sks remote control car` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/remote_control_car_flux_lora_500_style/tree/main) in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('cst7/remote_control_car_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks remote control car').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
Tristan/dclm-1b-raw-openbookqa-gs4 | Tristan | "2025-04-05T00:48:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:46:43Z" | ---
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] |
shanchen/s1-20250404_131427 | shanchen | "2025-04-05T00:47:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T17:15:18Z" | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: s1-20250404_131427
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
s1 - default
# Model Card for s1-20250404_131427
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="shanchen/s1-20250404_131427", 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/bitterman/s1/runs/3l97qq91)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.1
- Pytorch: 2.6.0+cu126
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
dgambettaphd/M_llm3_gen7_run0_W_doc1000_synt64_FRESH | dgambettaphd | "2025-04-05T00:46:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T00:46:28Z" | ---
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. 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] |
mika5883/ru_qwen_gec_Ag | mika5883 | "2025-04-05T00:43:35Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4",
"base_model:finetune:RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T23:28:19Z" | ---
base_model: RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4
library_name: transformers
model_name: ru_qwen_gec_Ag
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for ru_qwen_gec_Ag
This model is a fine-tuned version of [RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4](https://huggingface.co/RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4).
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="mika5883/ru_qwen_gec_Ag", 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/mika5883/huggingface/runs/yly4upwg)
This model was trained with SFT.
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- 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}}
}
``` |
zera09/qwen-dpo_v1 | zera09 | "2025-04-05T00:38:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T00:38:49Z" | ---
base_model: Qwen/Qwen2-VL-2B-Instruct
library_name: transformers
model_name: qwen-dpo_v1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen-dpo_v1
This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-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="zera09/qwen-dpo_v1", 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/zeramarveenlyngkhoi/huggingface/runs/euu5xwon)
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: 0.13.0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.0
## 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}}
}
``` |
Tristan/dclm-perplexity-correlations-1b-3-openbookqa-gs7 | Tristan | "2025-04-05T00:37:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:35:25Z" | ---
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]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### 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]
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Spestly/Athena-3-7B-GGUF | Spestly | "2025-04-05T00:36:36Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"en",
"base_model:Spestly/Athena-3-7B",
"base_model:quantized:Spestly/Athena-3-7B",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T00:36:35Z" | ---
base_model: Spestly/Athena-3-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- unsloth
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Spestly/Athena-3-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Athena-3-7B-i1-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/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-3-7B-GGUF/resolve/main/Athena-3-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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.
<!-- end -->
|
Tristan/dclm-perplexity-correlations-1b-3-openbookqa-gs1 | Tristan | "2025-04-05T00:35:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:33:23Z" | ---
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]
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## Uses
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### Direct Use
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[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. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- 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]
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## Model Card Contact
[More Information Needed] |
RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf | RichardErkhov | "2025-04-05T00:33:41Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T23:57:01Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Eco-gotest-r32-a32-epoch-2 - GGUF
- Model creator: https://huggingface.co/oodeh/
- Original model: https://huggingface.co/oodeh/Eco-gotest-r32-a32-epoch-2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Eco-gotest-r32-a32-epoch-2.Q2_K.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q2_K.gguf) | Q2_K | 1.27GB |
| [Eco-gotest-r32-a32-epoch-2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Eco-gotest-r32-a32-epoch-2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Eco-gotest-r32-a32-epoch-2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Eco-gotest-r32-a32-epoch-2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Eco-gotest-r32-a32-epoch-2.Q3_K.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q3_K.gguf) | Q3_K | 1.57GB |
| [Eco-gotest-r32-a32-epoch-2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Eco-gotest-r32-a32-epoch-2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Eco-gotest-r32-a32-epoch-2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Eco-gotest-r32-a32-epoch-2.Q4_0.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Eco-gotest-r32-a32-epoch-2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Eco-gotest-r32-a32-epoch-2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Eco-gotest-r32-a32-epoch-2.Q4_K.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q4_K.gguf) | Q4_K | 1.88GB |
| [Eco-gotest-r32-a32-epoch-2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Eco-gotest-r32-a32-epoch-2.Q4_1.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Eco-gotest-r32-a32-epoch-2.Q5_0.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Eco-gotest-r32-a32-epoch-2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Eco-gotest-r32-a32-epoch-2.Q5_K.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q5_K.gguf) | Q5_K | 2.16GB |
| [Eco-gotest-r32-a32-epoch-2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Eco-gotest-r32-a32-epoch-2.Q5_1.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Eco-gotest-r32-a32-epoch-2.Q6_K.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q6_K.gguf) | Q6_K | 2.46GB |
| [Eco-gotest-r32-a32-epoch-2.Q8_0.gguf](https://huggingface.co/RichardErkhov/oodeh_-_Eco-gotest-r32-a32-epoch-2-gguf/blob/main/Eco-gotest-r32-a32-epoch-2.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Tristan/multilingual-id-1b-raw-openbookqa-gs2 | Tristan | "2025-04-05T00:32:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:30:44Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Tristan/dclm-perplexity-correlations-410m-3-openbookqa-gs4 | Tristan | "2025-04-05T00:30:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:29:53Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Paramnoor/deepseek-medical-chat | Paramnoor | "2025-04-05T00:30:23Z" | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-04-04T22:45:24Z" | ---
license: apache-2.0
---
|
mradermacher/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-i1-GGUF | mradermacher | "2025-04-05T00:30:14Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2025-04-04T15:20:58Z" | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/DavidAU/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B
|
kamelcharaf/SFT-meta-Llama-3.1-8B-quant-mrd3 | kamelcharaf | "2025-04-05T00:30:12Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:kamelcharaf/Llama-3.1-8B-Instruct-quantized-4bit",
"base_model:finetune:kamelcharaf/Llama-3.1-8B-Instruct-quantized-4bit",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T00:20:10Z" | ---
base_model: kamelcharaf/Llama-3.1-8B-Instruct-quantized-4bit
library_name: transformers
model_name: SFT-meta-Llama-3.1-8B-quant-mrd3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SFT-meta-Llama-3.1-8B-quant-mrd3
This model is a fine-tuned version of [kamelcharaf/Llama-3.1-8B-Instruct-quantized-4bit](https://huggingface.co/kamelcharaf/Llama-3.1-8B-Instruct-quantized-4bit).
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="kamelcharaf/SFT-meta-Llama-3.1-8B-quant-mrd3", 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/kamel-charaf-epfl/huggingface/runs/2bydc6nk)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.0.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/fasttext-410m-raw-openbookqa-gs0 | Tristan | "2025-04-05T00:29:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:29:00Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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]
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[More Information Needed]
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<!-- 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).
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RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf | RichardErkhov | "2025-04-05T00:29:02Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T22:24:09Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.2-3B-MIS_v1.2 - GGUF
- Model creator: https://huggingface.co/suzii/
- Original model: https://huggingface.co/suzii/Llama-3.2-3B-MIS_v1.2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.2-3B-MIS_v1.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q2_K.gguf) | Q2_K | 1.27GB |
| [Llama-3.2-3B-MIS_v1.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Llama-3.2-3B-MIS_v1.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Llama-3.2-3B-MIS_v1.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Llama-3.2-3B-MIS_v1.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Llama-3.2-3B-MIS_v1.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q3_K.gguf) | Q3_K | 1.57GB |
| [Llama-3.2-3B-MIS_v1.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Llama-3.2-3B-MIS_v1.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Llama-3.2-3B-MIS_v1.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Llama-3.2-3B-MIS_v1.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Llama-3.2-3B-MIS_v1.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Llama-3.2-3B-MIS_v1.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Llama-3.2-3B-MIS_v1.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q4_K.gguf) | Q4_K | 1.88GB |
| [Llama-3.2-3B-MIS_v1.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Llama-3.2-3B-MIS_v1.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Llama-3.2-3B-MIS_v1.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Llama-3.2-3B-MIS_v1.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Llama-3.2-3B-MIS_v1.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q5_K.gguf) | Q5_K | 2.16GB |
| [Llama-3.2-3B-MIS_v1.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Llama-3.2-3B-MIS_v1.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Llama-3.2-3B-MIS_v1.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q6_K.gguf) | Q6_K | 2.46GB |
| [Llama-3.2-3B-MIS_v1.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/suzii_-_Llama-3.2-3B-MIS_v1.2-gguf/blob/main/Llama-3.2-3B-MIS_v1.2.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
Tristan/dclm-random-410m-openbookqa-gs10 | Tristan | "2025-04-05T00:28:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:28:07Z" | ---
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]
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<!-- Provide the basic links for the model. -->
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[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
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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).
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RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf | RichardErkhov | "2025-04-05T00:27:08Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T21:00:11Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
kbtg-kpoint-v1-fused - GGUF
- Model creator: https://huggingface.co/katopz/
- Original model: https://huggingface.co/katopz/kbtg-kpoint-v1-fused/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [kbtg-kpoint-v1-fused.Q2_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q2_K.gguf) | Q2_K | 1.27GB |
| [kbtg-kpoint-v1-fused.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [kbtg-kpoint-v1-fused.IQ3_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [kbtg-kpoint-v1-fused.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [kbtg-kpoint-v1-fused.IQ3_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [kbtg-kpoint-v1-fused.Q3_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q3_K.gguf) | Q3_K | 1.57GB |
| [kbtg-kpoint-v1-fused.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [kbtg-kpoint-v1-fused.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [kbtg-kpoint-v1-fused.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [kbtg-kpoint-v1-fused.Q4_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q4_0.gguf) | Q4_0 | 1.79GB |
| [kbtg-kpoint-v1-fused.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [kbtg-kpoint-v1-fused.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [kbtg-kpoint-v1-fused.Q4_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q4_K.gguf) | Q4_K | 1.88GB |
| [kbtg-kpoint-v1-fused.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [kbtg-kpoint-v1-fused.Q4_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q4_1.gguf) | Q4_1 | 1.95GB |
| [kbtg-kpoint-v1-fused.Q5_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q5_0.gguf) | Q5_0 | 2.11GB |
| [kbtg-kpoint-v1-fused.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [kbtg-kpoint-v1-fused.Q5_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q5_K.gguf) | Q5_K | 2.16GB |
| [kbtg-kpoint-v1-fused.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [kbtg-kpoint-v1-fused.Q5_1.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q5_1.gguf) | Q5_1 | 2.28GB |
| [kbtg-kpoint-v1-fused.Q6_K.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q6_K.gguf) | Q6_K | 2.46GB |
| [kbtg-kpoint-v1-fused.Q8_0.gguf](https://huggingface.co/RichardErkhov/katopz_-_kbtg-kpoint-v1-fused-gguf/blob/main/kbtg-kpoint-v1-fused.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
license: llama3.2
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base_model: meta-llama/Llama-3.2-3B-Instruct
---
# katopz/kbtg-kpoint-v1-fused
The Model [katopz/kbtg-kpoint-v1-fused](https://huggingface.co/katopz/kbtg-kpoint-v1-fused) was converted to MLX format from [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using mlx-lm version **0.19.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("katopz/kbtg-kpoint-v1-fused")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Tristan/v2-1b-raw-openbookqa-gs3 | Tristan | "2025-04-05T00:22:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:19:53Z" | ---
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]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **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
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[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]
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- **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]
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## Model Card Contact
[More Information Needed] |
taha454/a2c-PandaReachDense-v3 | taha454 | "2025-04-05T00:18:18Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-05T00:01:24Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.16
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Tristan/dclm-random-160m-raw-openbookqa-gs9 | Tristan | "2025-04-05T00:17:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:17:18Z" | ---
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
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[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] |
Manal0809/Mistral_nemo_calibrated_f1enhanced_full_oldinstruct_best_v3 | Manal0809 | "2025-04-05T00:17:09Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:adapter:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"region:us"
] | null | "2025-04-05T00:16:59Z" | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
library_name: peft
---
# 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. -->
- **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. -->
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- **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
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## Bias, Risks, and Limitations
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[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
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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### Framework versions
- PEFT 0.15.1 |
marcuscedricridia/Mixmix-LlaMAX3.2-1B-LORA | marcuscedricridia | "2025-04-05T00:14:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T00:14:27Z" | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** marcuscedricridia
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama 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)
|
Tristan/dclm-random-160m-raw-openbookqa-gs7 | Tristan | "2025-04-05T00:14:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:13:43Z" | ---
library_name: transformers
tags: []
---
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Tristan/dclm-id-160m-openbookqa-gs10 | Tristan | "2025-04-05T00:13:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:13:20Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Tristan/dclm-random-1b-openbookqa-gs11 | Tristan | "2025-04-05T00:12:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:10:43Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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schuler/experimental-JP47D62F | schuler | "2025-04-05T00:12:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"kphi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:11:19Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf | RichardErkhov | "2025-04-05T00:11:02Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T23:33:22Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3.2_3b_instruct_qall_lr_small - GGUF
- Model creator: https://huggingface.co/readerbench/
- Original model: https://huggingface.co/readerbench/llama3.2_3b_instruct_qall_lr_small/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama3.2_3b_instruct_qall_lr_small.Q2_K.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama3.2_3b_instruct_qall_lr_small.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama3.2_3b_instruct_qall_lr_small.IQ3_S.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama3.2_3b_instruct_qall_lr_small.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama3.2_3b_instruct_qall_lr_small.IQ3_M.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama3.2_3b_instruct_qall_lr_small.Q3_K.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama3.2_3b_instruct_qall_lr_small.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama3.2_3b_instruct_qall_lr_small.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama3.2_3b_instruct_qall_lr_small.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama3.2_3b_instruct_qall_lr_small.Q4_0.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama3.2_3b_instruct_qall_lr_small.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama3.2_3b_instruct_qall_lr_small.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama3.2_3b_instruct_qall_lr_small.Q4_K.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama3.2_3b_instruct_qall_lr_small.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama3.2_3b_instruct_qall_lr_small.Q4_1.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama3.2_3b_instruct_qall_lr_small.Q5_0.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama3.2_3b_instruct_qall_lr_small.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama3.2_3b_instruct_qall_lr_small.Q5_K.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama3.2_3b_instruct_qall_lr_small.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama3.2_3b_instruct_qall_lr_small.Q5_1.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama3.2_3b_instruct_qall_lr_small.Q6_K.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama3.2_3b_instruct_qall_lr_small.Q8_0.gguf](https://huggingface.co/RichardErkhov/readerbench_-_llama3.2_3b_instruct_qall_lr_small-gguf/blob/main/llama3.2_3b_instruct_qall_lr_small.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
armansakif/movieRatingBert | armansakif | "2025-04-05T00:10:44Z" | 21 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"pytorch",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-16T06:44:31Z" | ---
tags:
- transformers
- pytorch
- text-classification
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
---
|
Tristan/dclm-id-410m-openbookqa-gs3 | Tristan | "2025-04-05T00:10:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:09:49Z" | ---
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]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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[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
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[More Information Needed]
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- **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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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stanpony/tinylm33M-stella-1sent_5clust-2025-04-04-23-41_full | stanpony | "2025-04-05T00:10:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:10:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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]
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<!-- Provide the basic links for the model. -->
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## How to Get Started with the Model
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[More Information Needed]
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<!-- This should link to a Dataset Card if possible. -->
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### Results
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#### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf | RichardErkhov | "2025-04-05T00:09:46Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T20:52:25Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3.2-3B-adminguide - GGUF
- Model creator: https://huggingface.co/lliu01/
- Original model: https://huggingface.co/lliu01/llama-3.2-3B-adminguide/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3B-adminguide.Q2_K.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3B-adminguide.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3B-adminguide.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3B-adminguide.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3B-adminguide.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3B-adminguide.Q3_K.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3B-adminguide.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3B-adminguide.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3B-adminguide.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3B-adminguide.Q4_0.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3B-adminguide.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3B-adminguide.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3B-adminguide.Q4_K.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3B-adminguide.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3B-adminguide.Q4_1.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3B-adminguide.Q5_0.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3B-adminguide.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3B-adminguide.Q5_K.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3B-adminguide.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3B-adminguide.Q5_1.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3B-adminguide.Q6_K.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3B-adminguide.Q8_0.gguf](https://huggingface.co/RichardErkhov/lliu01_-_llama-3.2-3B-adminguide-gguf/blob/main/llama-3.2-3B-adminguide.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
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|
Tristan/dclm-410m-raw-openbookqa-gs9 | Tristan | "2025-04-05T00:09:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:08:53Z" | ---
library_name: transformers
tags: []
---
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Tristan/dclm-perplexity-correlations-410m-3-openbookqa-gs11 | Tristan | "2025-04-05T00:08:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:08:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Tristan/multilingual-1b-raw-openbookqa-gs1 | Tristan | "2025-04-05T00:06:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:04:29Z" | ---
library_name: transformers
tags: []
---
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tech20200/dragonman | tech20200 | "2025-04-05T00:06:13Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2025-04-04T21:40:55Z" | ---
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
--- |
Tristan/dclm-1b-raw-openbookqa-gs5 | Tristan | "2025-04-05T00:04:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:02:21Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf | RichardErkhov | "2025-04-05T00:04:19Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T20:53:21Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
my_Llama-3.2-3B-Instruct - GGUF
- Model creator: https://huggingface.co/pavan01729/
- Original model: https://huggingface.co/pavan01729/my_Llama-3.2-3B-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [my_Llama-3.2-3B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q2_K.gguf) | Q2_K | 1.27GB |
| [my_Llama-3.2-3B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [my_Llama-3.2-3B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [my_Llama-3.2-3B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [my_Llama-3.2-3B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [my_Llama-3.2-3B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q3_K.gguf) | Q3_K | 1.57GB |
| [my_Llama-3.2-3B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [my_Llama-3.2-3B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [my_Llama-3.2-3B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [my_Llama-3.2-3B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q4_0.gguf) | Q4_0 | 1.79GB |
| [my_Llama-3.2-3B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [my_Llama-3.2-3B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [my_Llama-3.2-3B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q4_K.gguf) | Q4_K | 1.88GB |
| [my_Llama-3.2-3B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [my_Llama-3.2-3B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q4_1.gguf) | Q4_1 | 1.95GB |
| [my_Llama-3.2-3B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q5_0.gguf) | Q5_0 | 2.11GB |
| [my_Llama-3.2-3B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [my_Llama-3.2-3B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q5_K.gguf) | Q5_K | 2.16GB |
| [my_Llama-3.2-3B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [my_Llama-3.2-3B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q5_1.gguf) | Q5_1 | 2.28GB |
| [my_Llama-3.2-3B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q6_K.gguf) | Q6_K | 2.46GB |
| [my_Llama-3.2-3B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/pavan01729_-_my_Llama-3.2-3B-Instruct-gguf/blob/main/my_Llama-3.2-3B-Instruct.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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|
cesun/cbllm-generation | cesun | "2025-04-05T00:03:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"InterpretableLLMs",
"text-generation",
"arxiv:2412.07992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-12-09T20:07:06Z" | ---
license: apache-2.0
pipeline_tag: text-generation
tags:
- InterpretableLLMs
library_name: transformers
---
# Concept Bottleneck Large Language Models
This repository contains the Concept Bottleneck Large Language Model (CB-LLM) presented in [Concept Bottleneck Large Language Models](https://arxiv.org/abs/2412.07992).
[Project Website](https://lilywenglab.github.io/CB-LLMs/)
Code: [https://github.com/Trustworthy-ML-Lab/CB-LLMs](https://github.com/Trustworthy-ML-Lab/CB-LLMs)
This model offers inherent interpretability and controllability in text generation. See the linked paper and GitHub repository for details on training and usage. |
Amouss3E/Cocoa-diseases-classifier | Amouss3E | "2025-04-05T00:03:16Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-05T00:03:16Z" | ---
license: apache-2.0
---
|
Tristan/v2-410m-raw-openbookqa-gs2 | Tristan | "2025-04-05T00:01:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T00:00:43Z" | ---
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]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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Tristan/dclm-random-410m-openbookqa-gs5 | Tristan | "2025-04-05T00:00:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:59:49Z" | ---
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]
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[More Information Needed]
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<!-- 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
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF | mradermacher | "2025-04-04T23:57:32Z" | 1,114 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksLab/Wordsmith-V3.0-LLaMa-70B",
"base_model:quantized:TareksLab/Wordsmith-V3.0-LLaMa-70B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-02T21:59:55Z" | ---
base_model: TareksLab/Wordsmith-V3.0-LLaMa-70B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TareksLab/Wordsmith-V3.0-LLaMa-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-i1-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/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Wordsmith-V3.0-LLaMa-70B-GGUF/resolve/main/Wordsmith-V3.0-LLaMa-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
Tristan/v2-160m-raw-openbookqa-gs8 | Tristan | "2025-04-04T23:57:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:56:46Z" | ---
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.
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linf545/LLaMA_RAG_lora_lr1e5_epo3_rank8_PLOS_0328 | linf545 | "2025-04-04T23:56:47Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-29T11:33:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
may have repeat sentense issue due to either overfitting or lack of EOT tocken.
### 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.
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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[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]
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## Technical Specifications [optional]
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RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf | RichardErkhov | "2025-04-04T23:56:28Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T22:41:48Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
my-Llama-3.2-3B-Instruct - GGUF
- Model creator: https://huggingface.co/frostsg/
- Original model: https://huggingface.co/frostsg/my-Llama-3.2-3B-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [my-Llama-3.2-3B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q2_K.gguf) | Q2_K | 1.27GB |
| [my-Llama-3.2-3B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [my-Llama-3.2-3B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [my-Llama-3.2-3B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [my-Llama-3.2-3B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [my-Llama-3.2-3B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q3_K.gguf) | Q3_K | 1.57GB |
| [my-Llama-3.2-3B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [my-Llama-3.2-3B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [my-Llama-3.2-3B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [my-Llama-3.2-3B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q4_0.gguf) | Q4_0 | 1.79GB |
| [my-Llama-3.2-3B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [my-Llama-3.2-3B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [my-Llama-3.2-3B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q4_K.gguf) | Q4_K | 1.88GB |
| [my-Llama-3.2-3B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [my-Llama-3.2-3B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q4_1.gguf) | Q4_1 | 1.95GB |
| [my-Llama-3.2-3B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q5_0.gguf) | Q5_0 | 2.11GB |
| [my-Llama-3.2-3B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [my-Llama-3.2-3B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q5_K.gguf) | Q5_K | 2.16GB |
| [my-Llama-3.2-3B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [my-Llama-3.2-3B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q5_1.gguf) | Q5_1 | 2.28GB |
| [my-Llama-3.2-3B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q6_K.gguf) | Q6_K | 2.46GB |
| [my-Llama-3.2-3B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/frostsg_-_my-Llama-3.2-3B-Instruct-gguf/blob/main/my-Llama-3.2-3B-Instruct.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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]
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#### Preprocessing [optional]
[More Information Needed]
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- **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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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## 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]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
lesso18/7ecf58d5-854c-42a7-aeee-3384e777d811 | lesso18 | "2025-04-04T23:54:02Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"region:us"
] | null | "2025-04-04T21:38:55Z" | ---
library_name: peft
license: other
base_model: huggyllama/llama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7ecf58d5-854c-42a7-aeee-3384e777d811
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
adapter: lora
base_model: huggyllama/llama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 300ce98114b271da_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/300ce98114b271da_train_data.json
type:
field_input: Moreinfo
field_instruction: Position
field_output: CV
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso18/7ecf58d5-854c-42a7-aeee-3384e777d811
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000218
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/300ce98114b271da_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 180
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a57f53f7-732a-4dcd-9a82-f085c1849c16
wandb_project: 18a
wandb_run: your_name
wandb_runid: a57f53f7-732a-4dcd-9a82-f085c1849c16
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7ecf58d5-854c-42a7-aeee-3384e777d811
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5940
## 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: 0.000218
- train_batch_size: 4
- eval_batch_size: 4
- seed: 180
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 0.8706 |
| 0.5928 | 0.0805 | 500 | 0.5940 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Tristan/dclm-id-160m-raw-openbookqa-gs6 | Tristan | "2025-04-04T23:53:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:53:00Z" | ---
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.
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### Downstream Use [optional]
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[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
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[More Information Needed]
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### Training Data
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[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).
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- **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. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Tristan/dclm-160m-raw-openbookqa-gs2 | Tristan | "2025-04-04T23:52:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:52:32Z" | ---
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
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[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. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Tristan/multilingual-id-410m-raw-openbookqa-gs5 | Tristan | "2025-04-04T23:52:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:51:50Z" | ---
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]
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[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] |
mvbooks/fnkydwst | mvbooks | "2025-04-04T23:51:51Z" | 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 | "2025-04-04T22:43:08Z" | ---
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: FNKYDWST
---
# Fnkydwst
<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 `FNKYDWST` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "FNKYDWST",
"lora_weights": "https://huggingface.co/mvbooks/fnkydwst/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('mvbooks/fnkydwst', weight_name='lora.safetensors')
image = pipeline('FNKYDWST').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/mvbooks/fnkydwst/discussions) to add images that show off what you’ve made with this LoRA.
|
Tristan/multilingual-1b-raw-openbookqa-gs7 | Tristan | "2025-04-04T23:51:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:49:52Z" | ---
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] |
Tristan/fasttext-410m-raw-openbookqa-gs11 | Tristan | "2025-04-04T23:49:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:49:06Z" | ---
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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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. -->
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#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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## 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]
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## Technical Specifications [optional]
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Tristan/dclm-id-410m-raw-openbookqa-gs0 | Tristan | "2025-04-04T23:49:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:48:17Z" | ---
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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
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## 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. -->
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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
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jssky/66586bc8-e4f0-471a-9959-0338804fc613 | jssky | "2025-04-04T23:48:52Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:adapter:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | null | "2025-04-04T15:01:01Z" | ---
library_name: peft
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 66586bc8-e4f0-471a-9959-0338804fc613
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: `0.6.0`
```yaml
adapter: lora
base_model: lmsys/vicuna-7b-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 29c1d7f6aa954ac1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/29c1d7f6aa954ac1_train_data.json
type:
field_input: reasoning (reasoning_content)
field_instruction: question
field_output: response (content)
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: jssky/66586bc8-e4f0-471a-9959-0338804fc613
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 256
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_inference_mode: true
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/29c1d7f6aa954ac1_train_data.json
model_type: AutoModelForCausalLM
modules_to_save: lm_head
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
peft_use_rslora: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 85283dc5-f9c6-494a-9560-8d768b99931f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 85283dc5-f9c6-494a-9560-8d768b99931f
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 66586bc8-e4f0-471a-9959-0338804fc613
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6581
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch_fused 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: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6702 | 0.8019 | 500 | 0.6581 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
aysey/trained_weights25 | aysey | "2025-04-04T23:46:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:36:38Z" | ---
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. 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] |
amit-huggingface2/Taxi-v3 | amit-huggingface2 | "2025-04-04T23:44:56Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-04T23:41:43Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.42 +/- 2.78
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="amit-huggingface2/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
IParraMartin/impossible-llms-indonesian-natural-2 | IParraMartin | "2025-04-04T23:44:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:01:16Z" | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: impossible-llms-indonesian-natural-2
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. -->
# impossible-llms-indonesian-natural-2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1338
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 84.7297 | 0.1961 | 10 | 9.5314 |
| 73.1794 | 0.3922 | 20 | 8.6491 |
| 68.8442 | 0.5882 | 30 | 8.4151 |
| 66.612 | 0.7843 | 40 | 8.1010 |
| 64.4903 | 0.9804 | 50 | 7.8329 |
| 62.2247 | 1.1765 | 60 | 7.5803 |
| 60.2017 | 1.3725 | 70 | 7.3236 |
| 58.1088 | 1.5686 | 80 | 7.0402 |
| 55.7028 | 1.7647 | 90 | 6.7708 |
| 53.9051 | 1.9608 | 100 | 6.5152 |
| 51.7145 | 2.1569 | 110 | 6.2839 |
| 49.8776 | 2.3529 | 120 | 6.0924 |
| 48.4837 | 2.5490 | 130 | 5.9626 |
| 48.0675 | 2.7451 | 140 | 5.8906 |
| 47.143 | 2.9412 | 150 | 5.8363 |
| 46.9472 | 3.1373 | 160 | 5.8036 |
| 46.6388 | 3.3333 | 170 | 5.7647 |
| 46.346 | 3.5294 | 180 | 5.7351 |
| 46.1808 | 3.7255 | 190 | 5.7014 |
| 45.8102 | 3.9216 | 200 | 5.6844 |
| 45.5526 | 4.1176 | 210 | 5.6568 |
| 45.2664 | 4.3137 | 220 | 5.6320 |
| 45.2098 | 4.5098 | 230 | 5.6166 |
| 44.9982 | 4.7059 | 240 | 5.6027 |
| 45.0693 | 4.9020 | 250 | 5.5709 |
| 44.6675 | 5.0980 | 260 | 5.5500 |
| 44.4332 | 5.2941 | 270 | 5.5314 |
| 44.2594 | 5.4902 | 280 | 5.5096 |
| 44.2192 | 5.6863 | 290 | 5.4926 |
| 43.965 | 5.8824 | 300 | 5.4764 |
| 43.8303 | 6.0784 | 310 | 5.4613 |
| 43.5255 | 6.2745 | 320 | 5.4414 |
| 43.3866 | 6.4706 | 330 | 5.4273 |
| 43.3281 | 6.6667 | 340 | 5.4114 |
| 43.3176 | 6.8627 | 350 | 5.3917 |
| 43.0143 | 7.0588 | 360 | 5.3752 |
| 42.6423 | 7.2549 | 370 | 5.3594 |
| 42.5939 | 7.4510 | 380 | 5.3370 |
| 42.4736 | 7.6471 | 390 | 5.3186 |
| 42.385 | 7.8431 | 400 | 5.2957 |
| 42.1624 | 8.0392 | 410 | 5.2758 |
| 41.7678 | 8.2353 | 420 | 5.2586 |
| 41.729 | 8.4314 | 430 | 5.2408 |
| 41.5099 | 8.6275 | 440 | 5.2230 |
| 41.5027 | 8.8235 | 450 | 5.2057 |
| 41.4235 | 9.0196 | 460 | 5.1848 |
| 40.9086 | 9.2157 | 470 | 5.1675 |
| 40.7761 | 9.4118 | 480 | 5.1491 |
| 40.5465 | 9.6078 | 490 | 5.1327 |
| 40.495 | 9.8039 | 500 | 5.1169 |
| 40.7107 | 10.0 | 510 | 5.1024 |
| 40.0136 | 10.1961 | 520 | 5.0873 |
| 40.022 | 10.3922 | 530 | 5.0751 |
| 39.9582 | 10.5882 | 540 | 5.0592 |
| 39.8591 | 10.7843 | 550 | 5.0520 |
| 39.7606 | 10.9804 | 560 | 5.0336 |
| 39.2016 | 11.1765 | 570 | 5.0170 |
| 39.3337 | 11.3725 | 580 | 5.0121 |
| 39.161 | 11.5686 | 590 | 4.9984 |
| 39.1951 | 11.7647 | 600 | 4.9843 |
| 39.0602 | 11.9608 | 610 | 4.9730 |
| 38.5559 | 12.1569 | 620 | 4.9637 |
| 38.3834 | 12.3529 | 630 | 4.9548 |
| 38.6287 | 12.5490 | 640 | 4.9444 |
| 38.6753 | 12.7451 | 650 | 4.9364 |
| 38.5492 | 12.9412 | 660 | 4.9276 |
| 38.1021 | 13.1373 | 670 | 4.9168 |
| 37.7665 | 13.3333 | 680 | 4.9156 |
| 38.0332 | 13.5294 | 690 | 4.9100 |
| 37.8571 | 13.7255 | 700 | 4.8972 |
| 38.0074 | 13.9216 | 710 | 4.8913 |
| 37.5119 | 14.1176 | 720 | 4.8832 |
| 37.4612 | 14.3137 | 730 | 4.8802 |
| 37.4864 | 14.5098 | 740 | 4.8783 |
| 37.5756 | 14.7059 | 750 | 4.8683 |
| 37.3706 | 14.9020 | 760 | 4.8632 |
| 36.8773 | 15.0980 | 770 | 4.8583 |
| 36.8592 | 15.2941 | 780 | 4.8569 |
| 36.9136 | 15.4902 | 790 | 4.8478 |
| 36.7774 | 15.6863 | 800 | 4.8401 |
| 37.1 | 15.8824 | 810 | 4.8411 |
| 36.7667 | 16.0784 | 820 | 4.8358 |
| 36.1423 | 16.2745 | 830 | 4.8376 |
| 36.5286 | 16.4706 | 840 | 4.8333 |
| 36.4368 | 16.6667 | 850 | 4.8276 |
| 36.4203 | 16.8627 | 860 | 4.8269 |
| 36.2714 | 17.0588 | 870 | 4.8246 |
| 35.8795 | 17.2549 | 880 | 4.8238 |
| 35.9196 | 17.4510 | 890 | 4.8229 |
| 36.0759 | 17.6471 | 900 | 4.8185 |
| 35.8202 | 17.8431 | 910 | 4.8144 |
| 36.0106 | 18.0392 | 920 | 4.8097 |
| 35.4214 | 18.2353 | 930 | 4.8163 |
| 35.4904 | 18.4314 | 940 | 4.8148 |
| 35.5777 | 18.6275 | 950 | 4.8098 |
| 35.6351 | 18.8235 | 960 | 4.8081 |
| 35.4077 | 19.0196 | 970 | 4.8033 |
| 34.9854 | 19.2157 | 980 | 4.8136 |
| 34.9366 | 19.4118 | 990 | 4.8090 |
| 35.2059 | 19.6078 | 1000 | 4.8084 |
| 35.1656 | 19.8039 | 1010 | 4.8055 |
| 35.1316 | 20.0 | 1020 | 4.8019 |
| 34.6261 | 20.1961 | 1030 | 4.8116 |
| 34.6117 | 20.3922 | 1040 | 4.8147 |
| 34.5858 | 20.5882 | 1050 | 4.8125 |
| 34.8078 | 20.7843 | 1060 | 4.8048 |
| 34.7927 | 20.9804 | 1070 | 4.8022 |
| 34.0879 | 21.1765 | 1080 | 4.8142 |
| 34.2655 | 21.3725 | 1090 | 4.8208 |
| 34.38 | 21.5686 | 1100 | 4.8149 |
| 34.2573 | 21.7647 | 1110 | 4.8172 |
| 34.3967 | 21.9608 | 1120 | 4.8110 |
| 33.9123 | 22.1569 | 1130 | 4.8212 |
| 33.8351 | 22.3529 | 1140 | 4.8204 |
| 33.8564 | 22.5490 | 1150 | 4.8236 |
| 33.898 | 22.7451 | 1160 | 4.8199 |
| 33.9953 | 22.9412 | 1170 | 4.8178 |
| 33.6276 | 23.1373 | 1180 | 4.8308 |
| 33.4547 | 23.3333 | 1190 | 4.8326 |
| 33.4377 | 23.5294 | 1200 | 4.8346 |
| 33.486 | 23.7255 | 1210 | 4.8313 |
| 33.6291 | 23.9216 | 1220 | 4.8301 |
| 33.3305 | 24.1176 | 1230 | 4.8406 |
| 32.9878 | 24.3137 | 1240 | 4.8444 |
| 33.1407 | 24.5098 | 1250 | 4.8481 |
| 33.108 | 24.7059 | 1260 | 4.8438 |
| 33.1634 | 24.9020 | 1270 | 4.8423 |
| 32.9804 | 25.0980 | 1280 | 4.8543 |
| 32.703 | 25.2941 | 1290 | 4.8589 |
| 32.7139 | 25.4902 | 1300 | 4.8597 |
| 32.9069 | 25.6863 | 1310 | 4.8592 |
| 32.8476 | 25.8824 | 1320 | 4.8533 |
| 32.6531 | 26.0784 | 1330 | 4.8635 |
| 32.3455 | 26.2745 | 1340 | 4.8696 |
| 32.4098 | 26.4706 | 1350 | 4.8806 |
| 32.4029 | 26.6667 | 1360 | 4.8746 |
| 32.4019 | 26.8627 | 1370 | 4.8720 |
| 32.5715 | 27.0588 | 1380 | 4.8785 |
| 31.9365 | 27.2549 | 1390 | 4.8941 |
| 32.041 | 27.4510 | 1400 | 4.8941 |
| 32.0831 | 27.6471 | 1410 | 4.8905 |
| 32.2654 | 27.8431 | 1420 | 4.8932 |
| 32.1213 | 28.0392 | 1430 | 4.8968 |
| 31.6334 | 28.2353 | 1440 | 4.9070 |
| 31.6266 | 28.4314 | 1450 | 4.9075 |
| 31.8562 | 28.6275 | 1460 | 4.9120 |
| 31.9469 | 28.8235 | 1470 | 4.9083 |
| 31.8122 | 29.0196 | 1480 | 4.9127 |
| 31.4544 | 29.2157 | 1490 | 4.9270 |
| 31.4096 | 29.4118 | 1500 | 4.9276 |
| 31.5559 | 29.6078 | 1510 | 4.9274 |
| 31.562 | 29.8039 | 1520 | 4.9272 |
| 31.5562 | 30.0 | 1530 | 4.9215 |
| 31.0939 | 30.1961 | 1540 | 4.9410 |
| 31.2037 | 30.3922 | 1550 | 4.9410 |
| 31.2114 | 30.5882 | 1560 | 4.9466 |
| 31.2936 | 30.7843 | 1570 | 4.9455 |
| 31.2188 | 30.9804 | 1580 | 4.9427 |
| 30.8361 | 31.1765 | 1590 | 4.9597 |
| 30.7884 | 31.3725 | 1600 | 4.9646 |
| 31.0089 | 31.5686 | 1610 | 4.9654 |
| 30.925 | 31.7647 | 1620 | 4.9618 |
| 31.0424 | 31.9608 | 1630 | 4.9602 |
| 30.6739 | 32.1569 | 1640 | 4.9734 |
| 30.5163 | 32.3529 | 1650 | 4.9796 |
| 30.6914 | 32.5490 | 1660 | 4.9829 |
| 30.7679 | 32.7451 | 1670 | 4.9828 |
| 30.7733 | 32.9412 | 1680 | 4.9809 |
| 30.4603 | 33.1373 | 1690 | 4.9981 |
| 30.4064 | 33.3333 | 1700 | 4.9960 |
| 30.3439 | 33.5294 | 1710 | 5.0019 |
| 30.4548 | 33.7255 | 1720 | 5.0005 |
| 30.4959 | 33.9216 | 1730 | 4.9983 |
| 30.1379 | 34.1176 | 1740 | 5.0079 |
| 30.1587 | 34.3137 | 1750 | 5.0144 |
| 30.258 | 34.5098 | 1760 | 5.0189 |
| 30.2917 | 34.7059 | 1770 | 5.0175 |
| 30.2123 | 34.9020 | 1780 | 5.0164 |
| 30.0077 | 35.0980 | 1790 | 5.0274 |
| 29.9258 | 35.2941 | 1800 | 5.0306 |
| 29.9456 | 35.4902 | 1810 | 5.0354 |
| 29.9983 | 35.6863 | 1820 | 5.0352 |
| 30.0681 | 35.8824 | 1830 | 5.0339 |
| 29.9492 | 36.0784 | 1840 | 5.0400 |
| 29.6012 | 36.2745 | 1850 | 5.0477 |
| 29.7514 | 36.4706 | 1860 | 5.0462 |
| 29.904 | 36.6667 | 1870 | 5.0468 |
| 29.9275 | 36.8627 | 1880 | 5.0506 |
| 29.6707 | 37.0588 | 1890 | 5.0568 |
| 29.4821 | 37.2549 | 1900 | 5.0631 |
| 29.5917 | 37.4510 | 1910 | 5.0615 |
| 29.6788 | 37.6471 | 1920 | 5.0622 |
| 29.7676 | 37.8431 | 1930 | 5.0663 |
| 29.561 | 38.0392 | 1940 | 5.0724 |
| 29.4237 | 38.2353 | 1950 | 5.0748 |
| 29.4713 | 38.4314 | 1960 | 5.0716 |
| 29.3732 | 38.6275 | 1970 | 5.0776 |
| 29.6006 | 38.8235 | 1980 | 5.0781 |
| 29.4671 | 39.0196 | 1990 | 5.0775 |
| 29.1346 | 39.2157 | 2000 | 5.0846 |
| 29.2986 | 39.4118 | 2010 | 5.0896 |
| 29.3189 | 39.6078 | 2020 | 5.0915 |
| 29.4456 | 39.8039 | 2030 | 5.0851 |
| 29.3781 | 40.0 | 2040 | 5.0893 |
| 29.1553 | 40.1961 | 2050 | 5.0939 |
| 29.1709 | 40.3922 | 2060 | 5.0985 |
| 29.2741 | 40.5882 | 2070 | 5.0973 |
| 29.3194 | 40.7843 | 2080 | 5.0986 |
| 29.082 | 40.9804 | 2090 | 5.0982 |
| 29.0652 | 41.1765 | 2100 | 5.1026 |
| 29.1368 | 41.3725 | 2110 | 5.1078 |
| 28.9788 | 41.5686 | 2120 | 5.1068 |
| 29.1678 | 41.7647 | 2130 | 5.1069 |
| 29.0869 | 41.9608 | 2140 | 5.1092 |
| 28.9663 | 42.1569 | 2150 | 5.1101 |
| 29.0139 | 42.3529 | 2160 | 5.1152 |
| 29.0885 | 42.5490 | 2170 | 5.1158 |
| 29.0786 | 42.7451 | 2180 | 5.1164 |
| 28.8401 | 42.9412 | 2190 | 5.1151 |
| 29.0292 | 43.1373 | 2200 | 5.1191 |
| 28.9627 | 43.3333 | 2210 | 5.1206 |
| 29.0252 | 43.5294 | 2220 | 5.1205 |
| 28.8265 | 43.7255 | 2230 | 5.1214 |
| 28.7749 | 43.9216 | 2240 | 5.1230 |
| 28.8979 | 44.1176 | 2250 | 5.1220 |
| 28.9138 | 44.3137 | 2260 | 5.1241 |
| 28.8695 | 44.5098 | 2270 | 5.1245 |
| 28.8581 | 44.7059 | 2280 | 5.1253 |
| 28.915 | 44.9020 | 2290 | 5.1265 |
| 28.6429 | 45.0980 | 2300 | 5.1269 |
| 28.8379 | 45.2941 | 2310 | 5.1296 |
| 28.9643 | 45.4902 | 2320 | 5.1285 |
| 28.8979 | 45.6863 | 2330 | 5.1288 |
| 28.6452 | 45.8824 | 2340 | 5.1289 |
| 28.8159 | 46.0784 | 2350 | 5.1297 |
| 28.7517 | 46.2745 | 2360 | 5.1300 |
| 28.8303 | 46.4706 | 2370 | 5.1319 |
| 28.7431 | 46.6667 | 2380 | 5.1312 |
| 28.8072 | 46.8627 | 2390 | 5.1315 |
| 28.7044 | 47.0588 | 2400 | 5.1326 |
| 28.8027 | 47.2549 | 2410 | 5.1326 |
| 28.6748 | 47.4510 | 2420 | 5.1331 |
| 28.6775 | 47.6471 | 2430 | 5.1331 |
| 28.8215 | 47.8431 | 2440 | 5.1328 |
| 28.8102 | 48.0392 | 2450 | 5.1331 |
| 28.7214 | 48.2353 | 2460 | 5.1336 |
| 28.777 | 48.4314 | 2470 | 5.1337 |
| 28.7972 | 48.6275 | 2480 | 5.1337 |
| 28.7564 | 48.8235 | 2490 | 5.1336 |
| 28.7261 | 49.0196 | 2500 | 5.1337 |
| 28.7168 | 49.2157 | 2510 | 5.1338 |
| 28.7067 | 49.4118 | 2520 | 5.1338 |
| 28.7308 | 49.6078 | 2530 | 5.1338 |
| 28.7859 | 49.8039 | 2540 | 5.1338 |
| 28.7408 | 50.0 | 2550 | 5.1338 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.4.0+cu121
- Datasets 3.4.0
- Tokenizers 0.21.0
|
osimetha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_bellowing_ox | osimetha | "2025-04-04T23:43:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vigilant bellowing ox",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T05:02:35Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_bellowing_ox
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vigilant bellowing ox
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_bellowing_ox
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="osimetha/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_bellowing_ox", 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: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/fasttext-410m-raw-openbookqa-gs9 | Tristan | "2025-04-04T23:43:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:42:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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<!-- Relevant interpretability work for the model goes here -->
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<!-- 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]
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Tristan/dclm-random-160m-openbookqa-gs1 | Tristan | "2025-04-04T23:41:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:40:46Z" | ---
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
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[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]
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[More Information Needed]
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[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]
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- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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rillky/Lily | rillky | "2025-04-04T23:40:34Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-04T23:22:18Z" | ---
license: apache-2.0
---
|
Sofia-gb/fashionSigLIP-roturas3 | Sofia-gb | "2025-04-04T23:39:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | "2025-04-04T23:39:05Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tristan/fasttext-160m-openbookqa-gs3 | Tristan | "2025-04-04T23:38:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:38:26Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Shaleen123/MedicalEDI-8b-EDI-Reasoning-nft | Shaleen123 | "2025-04-04T23:36:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:32:55Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Tristan/dclm-160m-raw-openbookqa-gs11 | Tristan | "2025-04-04T23:35:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:35:31Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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[More Information Needed]
## Training Details
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diliash/emuLM-spt-noimg-rounded | diliash | "2025-04-04T23:34:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"lora_run_rounded_noimg_20250404_162108",
"20250404_162108",
"lora-finetuning",
"lora_run_rounded_noimg_20250404_160637",
"20250404_160637",
"lora_run_rounded_noimg_20250404_160306",
"20250404_160306",
"lora_run_rounded_noimg_20250404_160131",
"20250404_160131",
"lora_run_rounded_noimg_20250404_155922",
"20250404_155922",
"lora_run_rounded_noimg_20250404_155517",
"20250404_155517",
"lora_run_rounded_noimg_20250404_154242",
"20250404_154242",
"lora_run_rounded_noimg_20250404_154200",
"20250404_154200",
"lora_run_edgelabelled_colored_20250404_141612",
"20250404_141612",
"lora_run_edgelabelled_colored_20250404_134651",
"20250404_134651",
"lora_run_rounded_colored_20250403_214449",
"20250403_214449",
"lora_run_rounded_colored_20250403_195038",
"20250403_195038",
"lora_run_rounded_colored_20250403_194012",
"20250403_194012",
"lora_run_rounded_colored_20250403_135921",
"20250403_135921",
"lora_run_rounded_colored_20250403_121200",
"20250403_121200",
"lora_run_rounded_colored_20250403_103814",
"20250403_103814",
"lora_run_rounded_colored_20250403_090510",
"20250403_090510",
"lora_run_rounded_colored_20250403_073345",
"20250403_073345",
"lora_run_rounded_colored_20250402_234837",
"20250402_234837",
"lora_run_rounded_colored_20250402_231331",
"20250402_231331",
"lora_run_rounded_colored_20250402_205929",
"20250402_205929",
"lora_run_rounded_colored_20250402_205628",
"20250402_205628",
"generated_from_trainer",
"lora_run_rounded_colored_20250402_204950",
"20250402_204950",
"final-model",
"processor",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-11B-Vision-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T22:42:01Z" | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
- lora_run_rounded_noimg_20250404_162108
- '20250404_162108'
- lora-finetuning
- lora_run_rounded_noimg_20250404_160637
- '20250404_160637'
- lora_run_rounded_noimg_20250404_160306
- '20250404_160306'
- lora_run_rounded_noimg_20250404_160131
- '20250404_160131'
- lora_run_rounded_noimg_20250404_155922
- '20250404_155922'
- lora_run_rounded_noimg_20250404_155517
- '20250404_155517'
- lora_run_rounded_noimg_20250404_154242
- '20250404_154242'
- lora_run_rounded_noimg_20250404_154200
- '20250404_154200'
- lora_run_edgelabelled_colored_20250404_141612
- '20250404_141612'
- lora_run_edgelabelled_colored_20250404_134651
- '20250404_134651'
- lora_run_rounded_colored_20250403_214449
- '20250403_214449'
- lora_run_rounded_colored_20250403_195038
- '20250403_195038'
- lora_run_rounded_colored_20250403_194012
- '20250403_194012'
- lora_run_rounded_colored_20250403_135921
- '20250403_135921'
- lora_run_rounded_colored_20250403_121200
- '20250403_121200'
- lora_run_rounded_colored_20250403_103814
- '20250403_103814'
- lora_run_rounded_colored_20250403_090510
- '20250403_090510'
- lora_run_rounded_colored_20250403_073345
- '20250403_073345'
- lora_run_rounded_colored_20250402_234837
- '20250402_234837'
- lora_run_rounded_colored_20250402_231331
- '20250402_231331'
- lora_run_rounded_colored_20250402_205929
- '20250402_205929'
- lora_run_rounded_colored_20250402_205628
- '20250402_205628'
- generated_from_trainer
- lora_run_rounded_colored_20250402_204950
- '20250402_204950'
- final-model
- processor
model-index:
- name: checkpoints
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. -->
# checkpoints
This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) on the None 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- 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: 0.03
- num_epochs: 3
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Tristan/v2-sampled-labels-1b-raw-openbookqa-gs0 | Tristan | "2025-04-04T23:33:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:31:53Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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aysey/trained_weigths24 | aysey | "2025-04-04T23:32:52Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"region:us"
] | null | "2025-04-04T23:32:47Z" | ---
tags:
- trl
- sft
- generated_from_trainer
library_name: peft
base_model: merged_model
model-index:
- name: trained_weigths24
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. -->
# trained_weigths24
This model was trained from scratch on the None 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: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.19.1 |
Tristan/dclm-1b-raw-openbookqa-gs8 | Tristan | "2025-04-04T23:31:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:29:55Z" | ---
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]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- 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
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[More Information Needed]
## Training Details
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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[More Information Needed]
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cst7/grey_sloth_plushie_flux_lora_500_style | cst7 | "2025-04-04T23:28:01Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-04T23:13:52Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks grey sloth plushie
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - cst7/grey_sloth_plushie_flux_lora_500_style
<Gallery />
## Model description
These are cst7/grey_sloth_plushie_flux_lora_500_style DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? True.
## Trigger words
You should use `a photo of sks grey sloth plushie` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/grey_sloth_plushie_flux_lora_500_style/tree/main) in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('cst7/grey_sloth_plushie_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks grey sloth plushie').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
Tristan/dclm-1b-raw-openbookqa-gs9 | Tristan | "2025-04-04T23:27:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:25:57Z" | ---
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]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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]
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<!-- 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
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[More Information Needed]
#### Metrics
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[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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fuasfh1jjh1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_barky_skunk | fuasfh1jjh1 | "2025-04-04T23:25:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fanged barky skunk",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T09:54:17Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_barky_skunk
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fanged barky skunk
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_barky_skunk
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="fuasfh1jjh1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_barky_skunk", 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: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/dclm-id-1b-openbookqa-gs7 | Tristan | "2025-04-04T23:25:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:23:14Z" | ---
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]
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## 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]
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soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF | soumitsr | "2025-04-04T23:24:28Z" | 33 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:soumitsr/SmolLM2-135M-Instruct-article-digestor",
"base_model:quantized:soumitsr/SmolLM2-135M-Instruct-article-digestor",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-02T23:44:40Z" | ---
base_model: soumitsr/SmolLM2-135M-Instruct-article-digestor
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF
This model was converted to GGUF format from [`soumitsr/SmolLM2-135M-Instruct-article-digestor`](https://huggingface.co/soumitsr/SmolLM2-135M-Instruct-article-digestor) 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/soumitsr/SmolLM2-135M-Instruct-article-digestor) 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 soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-article-digestor-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-article-digestor-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 soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-article-digestor-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo soumitsr/SmolLM2-135M-Instruct-article-digestor-Q4_K_M-GGUF --hf-file smollm2-135m-instruct-article-digestor-q4_k_m.gguf -c 2048
```
|
shrenikb/spectral_soft_diff_top8_gsm8k | shrenikb | "2025-04-04T23:22:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:19:31Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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canertugrul/DeepSeek-R1-Distill-Qwen-14B-Tool-Use-Tokenizer_v3 | canertugrul | "2025-04-04T23:22:44Z" | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T23:22:41Z" | ---
library_name: transformers
tags:
- unsloth
---
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Tristan/multilingual-410m-raw-openbookqa-gs6 | Tristan | "2025-04-04T23:22:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:21:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Tristan/v2-sampled-labels-410m-raw-openbookqa-gs4 | Tristan | "2025-04-04T23:21:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:20:34Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## How to Get Started with the Model
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[More Information Needed]
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