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Tristan/dclm-sampled-labels-160m-openbookqa-gs10 | Tristan | "2025-04-04T23:18:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:17:50Z" | ---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Tristan/v2-sampled-labels-1b-raw-openbookqa-gs6 | Tristan | "2025-04-04T23:16:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:15:04Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tristan/dclm-id-160m-raw-openbookqa-gs9 | Tristan | "2025-04-04T23:14:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:13:54Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tristan/dclm-random-160m-openbookqa-gs2 | Tristan | "2025-04-04T23:13:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:13:31Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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cst7/fancy_boot_flux_lora_500_style | cst7 | "2025-04-04T23:13:40Z" | 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 | "2025-04-04T22:57:14Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks fancy boot
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/fancy_boot_flux_lora_500_style
<Gallery />
## Model description
These are cst7/fancy_boot_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 fancy boot` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/fancy_boot_flux_lora_500_style/tree/main) in the Files & versions tab.
## Use it with the [𧨠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/fancy_boot_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks fancy boot').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/multilingual-id-1b-raw-openbookqa-gs9 | Tristan | "2025-04-04T23:13:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:11:37Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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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
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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).
- **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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Lilliandra/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_fierce_chameleon | Lilliandra | "2025-04-04T23:13:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am yawning fierce chameleon",
"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 | "2025-04-04T16:43:25Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_fierce_chameleon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am yawning fierce chameleon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_fierce_chameleon
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="Lilliandra/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_fierce_chameleon", 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: 2.6.0
- 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 GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/dclm-random-410m-raw-openbookqa-gs9 | Tristan | "2025-04-04T23:11:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:10:47Z" | ---
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 π€ 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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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **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
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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]
### 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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[More Information Needed]
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mlrnu/klodmone_style_LoRA | mlrnu | "2025-04-04T23:11:16Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T23:11:08Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo collage in KLODMONE style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - mlrnu/klodmone_style_LoRA
<Gallery />
## Model description
These are mlrnu/klodmone_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo collage in KLODMONE style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](mlrnu/klodmone_style_LoRA/tree/main) them in the Files & versions tab.
## 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-random-1b-raw-openbookqa-gs5 | Tristan | "2025-04-04T23:10:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:08:40Z" | ---
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 π€ 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] |
SuperGera228/Airplane_in_BLUEPRINT_style_LoRA | SuperGera228 | "2025-04-04T23:08:53Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T21:36:42Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: Airplane in BLUEPRINT style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - SuperGera228/Airplane_in_BLUEPRINT_style_LoRA
<Gallery />
## Model description
These are SuperGera228/Airplane_in_BLUEPRINT_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use Airplane in BLUEPRINT style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](SuperGera228/Airplane_in_BLUEPRINT_style_LoRA/tree/main) them in the Files & versions tab.
## 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-sampled-labels-1b-openbookqa-gs6 | Tristan | "2025-04-04T23:08:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:06:43Z" | ---
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 π€ 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] |
Goddard25/fuenteneptmty | Goddard25 | "2025-04-04T23:07:44Z" | 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 | "2025-04-04T22:51:23Z" | ---
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: fuenteneptmty
---
# Fuenteneptmty
<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 `fuenteneptmty` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "fuenteneptmty",
"lora_weights": "https://huggingface.co/Goddard25/fuenteneptmty/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 [𧨠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('Goddard25/fuenteneptmty', weight_name='lora.safetensors')
image = pipeline('fuenteneptmty').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Goddard25/fuenteneptmty/discussions) to add images that show off what youβve made with this LoRA.
|
Tristan/dclm-1b-raw-openbookqa-gs1 | Tristan | "2025-04-04T23:06:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T23:04:41Z" | ---
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 π€ 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]
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[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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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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RichardErkhov/jrobador_-_MatIA-gguf | RichardErkhov | "2025-04-04T23:02:30Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T22:21:29Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MatIA - GGUF
- Model creator: https://huggingface.co/jrobador/
- Original model: https://huggingface.co/jrobador/MatIA/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [MatIA.Q2_K.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q2_K.gguf) | Q2_K | 1.27GB |
| [MatIA.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [MatIA.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [MatIA.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [MatIA.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [MatIA.Q3_K.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q3_K.gguf) | Q3_K | 1.57GB |
| [MatIA.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [MatIA.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [MatIA.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [MatIA.Q4_0.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q4_0.gguf) | Q4_0 | 1.79GB |
| [MatIA.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [MatIA.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [MatIA.Q4_K.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q4_K.gguf) | Q4_K | 1.88GB |
| [MatIA.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [MatIA.Q4_1.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q4_1.gguf) | Q4_1 | 1.95GB |
| [MatIA.Q5_0.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q5_0.gguf) | Q5_0 | 2.11GB |
| [MatIA.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [MatIA.Q5_K.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q5_K.gguf) | Q5_K | 2.16GB |
| [MatIA.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [MatIA.Q5_1.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q5_1.gguf) | Q5_1 | 2.28GB |
| [MatIA.Q6_K.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.Q6_K.gguf) | Q6_K | 2.46GB |
| [MatIA.Q8_0.gguf](https://huggingface.co/RichardErkhov/jrobador_-_MatIA-gguf/blob/main/MatIA.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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
<!-- 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
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[More Information Needed]
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[More Information Needed]
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## 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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|
Tristan/multilingual-410m-raw-openbookqa-gs10 | Tristan | "2025-04-04T22:59:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:59:07Z" | ---
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 π€ 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]
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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]
#### 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] |
lesso15/8c6f20cd-21e9-4592-a601-c866182f25f6 | lesso15 | "2025-04-04T22:59:21Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3.5-mini-instruct",
"base_model:adapter:unsloth/Phi-3.5-mini-instruct",
"license:mit",
"region:us"
] | null | "2025-04-04T21:32:04Z" | ---
library_name: peft
license: mit
base_model: unsloth/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8c6f20cd-21e9-4592-a601-c866182f25f6
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/Phi-3.5-mini-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a4b28db74a98908d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a4b28db74a98908d_train_data.json
type:
field_input: endings
field_instruction: ctx
field_output: input_formatted
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: lesso15/8c6f20cd-21e9-4592-a601-c866182f25f6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000215
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/a4b28db74a98908d_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: 150
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: 04cf94fb-07fa-4dfa-b7bd-70925a2037b0
wandb_project: 15a
wandb_run: your_name
wandb_runid: 04cf94fb-07fa-4dfa-b7bd-70925a2037b0
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8c6f20cd-21e9-4592-a601-c866182f25f6
This model is a fine-tuned version of [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 12.9216
## 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: 0.000215
- train_batch_size: 4
- eval_batch_size: 4
- seed: 150
- 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 | 0.0007 | 1 | 12.9233 |
| 12.928 | 0.3436 | 500 | 12.9216 |
### 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-perplexity-correlations-1b-3-openbookqa-gs3 | Tristan | "2025-04-04T22:59:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:57:09Z" | ---
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 π€ 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
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#### Preprocessing [optional]
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#### Training Hyperparameters
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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. -->
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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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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mradermacher/QwQ-R1984-32B-h-novel-GGUF | mradermacher | "2025-04-04T22:58:57Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:cs2764/QwQ-R1984-32B-h-novel",
"base_model:quantized:cs2764/QwQ-R1984-32B-h-novel",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T21:08:21Z" | ---
base_model: cs2764/QwQ-R1984-32B-h-novel
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/cs2764/QwQ-R1984-32B-h-novel
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QwQ-R1984-32B-h-novel-GGUF/resolve/main/QwQ-R1984-32B-h-novel.Q8_0.gguf) | Q8_0 | 34.9 | 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.
<!-- end -->
|
Tristan/dclm-sampled-labels-160m-openbookqa-gs3 | Tristan | "2025-04-04T22:57:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:56:40Z" | ---
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 π€ 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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### Model Sources [optional]
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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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### 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. -->
[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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## Glossary [optional]
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[More Information Needed]
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## Model Card Contact
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hZzy/qwen2.5-0.5b-expo-IPO-25-1 | hZzy | "2025-04-04T22:56:13Z" | 16 | 0 | null | [
"safetensors",
"qwen2",
"alignment-handbook",
"ndcg",
"trl",
"expo",
"generated_from_trainer",
"dataset:hZzy/train_pairwise_all_new4",
"base_model:hZzy/qwen2.5-0.5b-sft3-25-2",
"base_model:finetune:hZzy/qwen2.5-0.5b-sft3-25-2",
"license:apache-2.0",
"region:us"
] | null | "2025-03-12T17:05:33Z" | ---
license: apache-2.0
base_model: hZzy/qwen2.5-0.5b-sft3-25-2
tags:
- alignment-handbook
- ndcg
- trl
- expo
- generated_from_trainer
- trl
- expo
- generated_from_trainer
datasets:
- hZzy/train_pairwise_all_new4
model-index:
- name: qwen2.5-0.5b-expo-IPO-25-1
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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zhiyuzha-university-of-florida/huggingface/runs/ptj9w5ng)
# qwen2.5-0.5b-expo-IPO-25-1
This model is a fine-tuned version of [hZzy/qwen2.5-0.5b-sft3-25-2](https://huggingface.co/hZzy/qwen2.5-0.5b-sft3-25-2) on the hZzy/train_pairwise_all_new4 dataset.
It achieves the following results on the evaluation set:
- Loss: 44.8744
- Objective: 44.8557
- Reward Accuracy: 0.5884
- Logp Accuracy: 0.5632
- Log Diff Policy: 6.6358
- Chosen Logps: -132.9855
- Rejected Logps: -139.6213
- Chosen Rewards: -0.4551
- Rejected Rewards: -0.5182
- Logits: -2.2391
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 12
- total_train_batch_size: 288
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Objective | Reward Accuracy | Logp Accuracy | Log Diff Policy | Chosen Logps | Rejected Logps | Chosen Rewards | Rejected Rewards | Logits |
|:-------------:|:------:|:----:|:---------------:|:---------:|:---------------:|:-------------:|:---------------:|:------------:|:--------------:|:--------------:|:----------------:|:-------:|
| 48.4932 | 0.1577 | 50 | 48.8640 | 48.8817 | 0.5336 | 0.5246 | 1.4437 | -99.4633 | -100.9070 | -0.1199 | -0.1311 | -1.3149 |
| 46.098 | 0.3154 | 100 | 47.4286 | 47.0768 | 0.5682 | 0.5610 | 3.6136 | -120.9156 | -124.5292 | -0.3344 | -0.3673 | -1.5087 |
| 44.3116 | 0.4731 | 150 | 45.7856 | 45.6942 | 0.5850 | 0.5811 | 5.0661 | -113.3503 | -118.4164 | -0.2587 | -0.3062 | -1.6136 |
| 43.3978 | 0.6307 | 200 | 45.5774 | 45.2083 | 0.5973 | 0.5777 | 6.3228 | -133.1022 | -139.4250 | -0.4563 | -0.5162 | -1.9086 |
| 42.7061 | 0.7884 | 250 | 44.9593 | 44.7260 | 0.6046 | 0.5772 | 6.5003 | -124.7787 | -131.2790 | -0.3730 | -0.4348 | -1.9727 |
| 41.9962 | 0.9461 | 300 | 44.5164 | 44.4618 | 0.5979 | 0.5794 | 6.9872 | -129.9575 | -136.9447 | -0.4248 | -0.4914 | -2.0568 |
| 38.2458 | 1.1038 | 350 | 44.7698 | 44.6525 | 0.6007 | 0.5794 | 6.6454 | -127.8146 | -134.4600 | -0.4034 | -0.4666 | -2.0736 |
| 36.6528 | 1.2615 | 400 | 45.2601 | 44.9216 | 0.6040 | 0.5772 | 6.9298 | -135.8740 | -142.8038 | -0.4840 | -0.5500 | -2.1306 |
| 37.2127 | 1.4192 | 450 | 44.8450 | 44.9502 | 0.5962 | 0.5800 | 6.5044 | -129.3140 | -135.8184 | -0.4184 | -0.4802 | -2.1449 |
| 36.5389 | 1.5769 | 500 | 44.9225 | 44.7990 | 0.5872 | 0.5632 | 6.8611 | -139.9226 | -146.7837 | -0.5245 | -0.5898 | -2.2197 |
| 36.1702 | 1.7346 | 550 | 44.9264 | 44.6704 | 0.6035 | 0.5710 | 6.9227 | -140.5884 | -147.5112 | -0.5311 | -0.5971 | -2.2685 |
| 36.3218 | 1.8922 | 600 | 44.6893 | 44.7346 | 0.5973 | 0.5671 | 6.5700 | -129.7259 | -136.2959 | -0.4225 | -0.4850 | -2.2583 |
### Framework versions
- Transformers 4.42.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1
|
HailHydra/so100_t0 | HailHydra | "2025-04-04T22:55:28Z" | 0 | 0 | null | [
"safetensors",
"LeRobot",
"SO100",
"PicknPlace",
"robotics",
"en",
"dataset:abhisb/so100_51_ep",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | robotics | "2025-04-04T09:07:49Z" | ---
license: mit
datasets:
- abhisb/so100_51_ep
language:
- en
pipeline_tag: robotics
tags:
- LeRobot
- SO100
- PicknPlace
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
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### 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]
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[More Information Needed] |
TopovayaKatana/BOSCH_style_LoRA | TopovayaKatana | "2025-04-04T22:53:50Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T22:53:45Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo collage in BOSCH style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - TopovayaKatana/BOSCH_style_LoRA
<Gallery />
## Model description
These are TopovayaKatana/BOSCH_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo collage in BOSCH style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](TopovayaKatana/BOSCH_style_LoRA/tree/main) them in the Files & versions tab.
## 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] |
IParraMartin/impossible-llms-spanish-random-v1-1 | IParraMartin | "2025-04-04T22:52:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T20:45:34Z" | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: impossible-llms-spanish-random-v1-1
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-spanish-random-v1-1
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: 6.2989
## 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: 0
- 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 84.4246 | 0.2111 | 10 | 9.7407 |
| 75.3967 | 0.4222 | 20 | 9.1305 |
| 72.3352 | 0.6332 | 30 | 8.9389 |
| 70.9431 | 0.8443 | 40 | 8.7373 |
| 64.5564 | 1.0422 | 50 | 8.5127 |
| 67.0698 | 1.2533 | 60 | 8.2530 |
| 64.8037 | 1.4644 | 70 | 7.9438 |
| 62.1239 | 1.6755 | 80 | 7.6406 |
| 59.9505 | 1.8865 | 90 | 7.3259 |
| 53.7683 | 2.0844 | 100 | 7.0330 |
| 55.1851 | 2.2955 | 110 | 6.7911 |
| 53.3832 | 2.5066 | 120 | 6.6275 |
| 52.3148 | 2.7177 | 130 | 6.5373 |
| 51.8904 | 2.9288 | 140 | 6.4899 |
| 48.2862 | 3.1266 | 150 | 6.4608 |
| 51.3813 | 3.3377 | 160 | 6.4322 |
| 51.0232 | 3.5488 | 170 | 6.4018 |
| 50.6281 | 3.7599 | 180 | 6.3853 |
| 50.7188 | 3.9710 | 190 | 6.3500 |
| 47.0642 | 4.1689 | 200 | 6.3295 |
| 50.0102 | 4.3799 | 210 | 6.2954 |
| 49.7912 | 4.5910 | 220 | 6.2759 |
| 49.8849 | 4.8021 | 230 | 6.2529 |
| 46.5695 | 5.0 | 240 | 6.2307 |
| 49.2983 | 5.2111 | 250 | 6.2178 |
| 49.2503 | 5.4222 | 260 | 6.1945 |
| 48.9211 | 5.6332 | 270 | 6.1872 |
| 49.1161 | 5.8443 | 280 | 6.1699 |
| 45.6273 | 6.0422 | 290 | 6.1539 |
| 48.4393 | 6.2533 | 300 | 6.1409 |
| 48.5551 | 6.4644 | 310 | 6.1295 |
| 48.2569 | 6.6755 | 320 | 6.1167 |
| 48.3756 | 6.8865 | 330 | 6.1027 |
| 45.0783 | 7.0844 | 340 | 6.0940 |
| 47.8177 | 7.2955 | 350 | 6.0838 |
| 47.8354 | 7.5066 | 360 | 6.0730 |
| 47.853 | 7.7177 | 370 | 6.0604 |
| 47.7986 | 7.9288 | 380 | 6.0468 |
| 44.4384 | 8.1266 | 390 | 6.0403 |
| 47.2241 | 8.3377 | 400 | 6.0291 |
| 47.2953 | 8.5488 | 410 | 6.0169 |
| 47.2114 | 8.7599 | 420 | 6.0069 |
| 47.0619 | 8.9710 | 430 | 5.9967 |
| 43.8162 | 9.1689 | 440 | 5.9921 |
| 46.7649 | 9.3799 | 450 | 5.9806 |
| 46.5826 | 9.5910 | 460 | 5.9670 |
| 46.6559 | 9.8021 | 470 | 5.9626 |
| 43.7446 | 10.0 | 480 | 5.9582 |
| 46.1567 | 10.2111 | 490 | 5.9456 |
| 46.1748 | 10.4222 | 500 | 5.9384 |
| 46.1301 | 10.6332 | 510 | 5.9286 |
| 46.1467 | 10.8443 | 520 | 5.9234 |
| 43.0499 | 11.0422 | 530 | 5.9173 |
| 45.6468 | 11.2533 | 540 | 5.9122 |
| 45.6464 | 11.4644 | 550 | 5.9057 |
| 45.6376 | 11.6755 | 560 | 5.8983 |
| 45.6506 | 11.8865 | 570 | 5.8917 |
| 42.6499 | 12.0844 | 580 | 5.8838 |
| 45.0362 | 12.2955 | 590 | 5.8809 |
| 45.2929 | 12.5066 | 600 | 5.8778 |
| 45.1755 | 12.7177 | 610 | 5.8673 |
| 45.1212 | 12.9288 | 620 | 5.8635 |
| 42.0539 | 13.1266 | 630 | 5.8607 |
| 44.5632 | 13.3377 | 640 | 5.8561 |
| 44.5651 | 13.5488 | 650 | 5.8515 |
| 44.7611 | 13.7599 | 660 | 5.8466 |
| 44.7529 | 13.9710 | 670 | 5.8420 |
| 41.656 | 14.1689 | 680 | 5.8444 |
| 44.2254 | 14.3799 | 690 | 5.8416 |
| 44.0627 | 14.5910 | 700 | 5.8384 |
| 44.3216 | 14.8021 | 710 | 5.8313 |
| 41.4656 | 15.0 | 720 | 5.8311 |
| 43.8511 | 15.2111 | 730 | 5.8305 |
| 43.6645 | 15.4222 | 740 | 5.8304 |
| 43.6898 | 15.6332 | 750 | 5.8265 |
| 43.8493 | 15.8443 | 760 | 5.8235 |
| 40.9721 | 16.0422 | 770 | 5.8223 |
| 43.3423 | 16.2533 | 780 | 5.8259 |
| 43.2435 | 16.4644 | 790 | 5.8244 |
| 43.4439 | 16.6755 | 800 | 5.8243 |
| 43.2729 | 16.8865 | 810 | 5.8167 |
| 40.5513 | 17.0844 | 820 | 5.8214 |
| 42.9218 | 17.2955 | 830 | 5.8247 |
| 42.9142 | 17.5066 | 840 | 5.8220 |
| 42.8207 | 17.7177 | 850 | 5.8230 |
| 42.9112 | 17.9288 | 860 | 5.8186 |
| 39.7839 | 18.1266 | 870 | 5.8281 |
| 42.4705 | 18.3377 | 880 | 5.8286 |
| 42.4754 | 18.5488 | 890 | 5.8268 |
| 42.388 | 18.7599 | 900 | 5.8248 |
| 42.528 | 18.9710 | 910 | 5.8206 |
| 39.3982 | 19.1689 | 920 | 5.8316 |
| 41.8835 | 19.3799 | 930 | 5.8331 |
| 42.0069 | 19.5910 | 940 | 5.8348 |
| 42.081 | 19.8021 | 950 | 5.8319 |
| 39.3583 | 20.0 | 960 | 5.8336 |
| 41.4834 | 20.2111 | 970 | 5.8413 |
| 41.6045 | 20.4222 | 980 | 5.8465 |
| 41.4159 | 20.6332 | 990 | 5.8456 |
| 41.6076 | 20.8443 | 1000 | 5.8426 |
| 38.8551 | 21.0422 | 1010 | 5.8456 |
| 40.9776 | 21.2533 | 1020 | 5.8593 |
| 41.0644 | 21.4644 | 1030 | 5.8596 |
| 41.1902 | 21.6755 | 1040 | 5.8590 |
| 41.1312 | 21.8865 | 1050 | 5.8593 |
| 38.227 | 22.0844 | 1060 | 5.8670 |
| 40.4807 | 22.2955 | 1070 | 5.8776 |
| 40.6887 | 22.5066 | 1080 | 5.8780 |
| 40.737 | 22.7177 | 1090 | 5.8755 |
| 40.7507 | 22.9288 | 1100 | 5.8762 |
| 37.8548 | 23.1266 | 1110 | 5.8860 |
| 40.0318 | 23.3377 | 1120 | 5.8976 |
| 40.2698 | 23.5488 | 1130 | 5.8951 |
| 40.2923 | 23.7599 | 1140 | 5.8960 |
| 40.2322 | 23.9710 | 1150 | 5.8998 |
| 37.3508 | 24.1689 | 1160 | 5.9089 |
| 39.7624 | 24.3799 | 1170 | 5.9149 |
| 39.849 | 24.5910 | 1180 | 5.9187 |
| 39.7565 | 24.8021 | 1190 | 5.9222 |
| 37.2275 | 25.0 | 1200 | 5.9174 |
| 39.0789 | 25.2111 | 1210 | 5.9379 |
| 39.2934 | 25.4222 | 1220 | 5.9459 |
| 39.4164 | 25.6332 | 1230 | 5.9394 |
| 39.4407 | 25.8443 | 1240 | 5.9392 |
| 36.9118 | 26.0422 | 1250 | 5.9496 |
| 38.9421 | 26.2533 | 1260 | 5.9699 |
| 38.9299 | 26.4644 | 1270 | 5.9667 |
| 38.7565 | 26.6755 | 1280 | 5.9684 |
| 38.974 | 26.8865 | 1290 | 5.9659 |
| 36.4567 | 27.0844 | 1300 | 5.9793 |
| 38.5171 | 27.2955 | 1310 | 5.9893 |
| 38.46 | 27.5066 | 1320 | 5.9913 |
| 38.537 | 27.7177 | 1330 | 5.9913 |
| 38.6521 | 27.9288 | 1340 | 5.9908 |
| 35.9328 | 28.1266 | 1350 | 6.0113 |
| 37.9973 | 28.3377 | 1360 | 6.0190 |
| 38.1685 | 28.5488 | 1370 | 6.0245 |
| 38.1577 | 28.7599 | 1380 | 6.0187 |
| 38.1878 | 28.9710 | 1390 | 6.0174 |
| 35.5106 | 29.1689 | 1400 | 6.0371 |
| 37.5767 | 29.3799 | 1410 | 6.0453 |
| 37.7632 | 29.5910 | 1420 | 6.0443 |
| 37.8554 | 29.8021 | 1430 | 6.0457 |
| 35.5995 | 30.0 | 1440 | 6.0424 |
| 37.307 | 30.2111 | 1450 | 6.0696 |
| 37.287 | 30.4222 | 1460 | 6.0747 |
| 37.3642 | 30.6332 | 1470 | 6.0748 |
| 37.6043 | 30.8443 | 1480 | 6.0687 |
| 35.1194 | 31.0422 | 1490 | 6.0765 |
| 36.9317 | 31.2533 | 1500 | 6.0949 |
| 37.1362 | 31.4644 | 1510 | 6.0950 |
| 37.0671 | 31.6755 | 1520 | 6.0989 |
| 37.2237 | 31.8865 | 1530 | 6.0944 |
| 34.6476 | 32.0844 | 1540 | 6.1155 |
| 36.707 | 32.2955 | 1550 | 6.1260 |
| 36.7806 | 32.5066 | 1560 | 6.1217 |
| 36.8449 | 32.7177 | 1570 | 6.1228 |
| 36.7109 | 32.9288 | 1580 | 6.1216 |
| 34.2811 | 33.1266 | 1590 | 6.1365 |
| 36.5273 | 33.3377 | 1600 | 6.1478 |
| 36.505 | 33.5488 | 1610 | 6.1423 |
| 36.5634 | 33.7599 | 1620 | 6.1455 |
| 36.3709 | 33.9710 | 1630 | 6.1448 |
| 33.9777 | 34.1689 | 1640 | 6.1651 |
| 36.1121 | 34.3799 | 1650 | 6.1709 |
| 36.2047 | 34.5910 | 1660 | 6.1601 |
| 36.3008 | 34.8021 | 1670 | 6.1670 |
| 33.9635 | 35.0 | 1680 | 6.1641 |
| 35.6621 | 35.2111 | 1690 | 6.1881 |
| 36.0521 | 35.4222 | 1700 | 6.1904 |
| 36.0516 | 35.6332 | 1710 | 6.1872 |
| 35.9789 | 35.8443 | 1720 | 6.1890 |
| 33.7365 | 36.0422 | 1730 | 6.1928 |
| 35.7488 | 36.2533 | 1740 | 6.2038 |
| 35.6586 | 36.4644 | 1750 | 6.2085 |
| 35.7664 | 36.6755 | 1760 | 6.2059 |
| 35.7609 | 36.8865 | 1770 | 6.2076 |
| 33.462 | 37.0844 | 1780 | 6.2206 |
| 35.4856 | 37.2955 | 1790 | 6.2274 |
| 35.4822 | 37.5066 | 1800 | 6.2228 |
| 35.5313 | 37.7177 | 1810 | 6.2235 |
| 35.5615 | 37.9288 | 1820 | 6.2242 |
| 33.1249 | 38.1266 | 1830 | 6.2336 |
| 35.264 | 38.3377 | 1840 | 6.2380 |
| 35.3654 | 38.5488 | 1850 | 6.2363 |
| 35.4033 | 38.7599 | 1860 | 6.2454 |
| 35.4364 | 38.9710 | 1870 | 6.2395 |
| 33.0375 | 39.1689 | 1880 | 6.2516 |
| 35.2673 | 39.3799 | 1890 | 6.2536 |
| 35.1057 | 39.5910 | 1900 | 6.2556 |
| 35.0845 | 39.8021 | 1910 | 6.2529 |
| 33.085 | 40.0 | 1920 | 6.2538 |
| 35.0653 | 40.2111 | 1930 | 6.2625 |
| 35.0239 | 40.4222 | 1940 | 6.2661 |
| 35.0348 | 40.6332 | 1950 | 6.2664 |
| 35.0705 | 40.8443 | 1960 | 6.2653 |
| 32.8909 | 41.0422 | 1970 | 6.2670 |
| 34.9337 | 41.2533 | 1980 | 6.2741 |
| 34.832 | 41.4644 | 1990 | 6.2751 |
| 34.9137 | 41.6755 | 2000 | 6.2740 |
| 34.9568 | 41.8865 | 2010 | 6.2759 |
| 32.7608 | 42.0844 | 2020 | 6.2795 |
| 34.7598 | 42.2955 | 2030 | 6.2826 |
| 34.8879 | 42.5066 | 2040 | 6.2834 |
| 34.7188 | 42.7177 | 2050 | 6.2815 |
| 35.027 | 42.9288 | 2060 | 6.2833 |
| 32.7301 | 43.1266 | 2070 | 6.2858 |
| 34.693 | 43.3377 | 2080 | 6.2879 |
| 34.7369 | 43.5488 | 2090 | 6.2883 |
| 34.7703 | 43.7599 | 2100 | 6.2879 |
| 34.8199 | 43.9710 | 2110 | 6.2892 |
| 32.5066 | 44.1689 | 2120 | 6.2912 |
| 34.7172 | 44.3799 | 2130 | 6.2929 |
| 34.8427 | 44.5910 | 2140 | 6.2927 |
| 34.656 | 44.8021 | 2150 | 6.2944 |
| 32.5422 | 45.0 | 2160 | 6.2934 |
| 34.7199 | 45.2111 | 2170 | 6.2949 |
| 34.6438 | 45.4222 | 2180 | 6.2961 |
| 34.6079 | 45.6332 | 2190 | 6.2967 |
| 34.6438 | 45.8443 | 2200 | 6.2970 |
| 32.5286 | 46.0422 | 2210 | 6.2982 |
| 34.6099 | 46.2533 | 2220 | 6.2979 |
| 34.6868 | 46.4644 | 2230 | 6.2961 |
| 34.5691 | 46.6755 | 2240 | 6.2976 |
| 34.7377 | 46.8865 | 2250 | 6.2986 |
| 32.4303 | 47.0844 | 2260 | 6.2985 |
| 34.6672 | 47.2955 | 2270 | 6.2983 |
| 34.5762 | 47.5066 | 2280 | 6.2983 |
| 34.5906 | 47.7177 | 2290 | 6.2984 |
| 34.6889 | 47.9288 | 2300 | 6.2987 |
| 32.4417 | 48.1266 | 2310 | 6.2990 |
| 34.7747 | 48.3377 | 2320 | 6.2990 |
| 34.6332 | 48.5488 | 2330 | 6.2989 |
| 34.5391 | 48.7599 | 2340 | 6.2989 |
| 34.6561 | 48.9710 | 2350 | 6.2989 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.4.0+cu121
- Datasets 3.4.0
- Tokenizers 0.21.0
|
ButterChicken98/pv_lb_v3 | ButterChicken98 | "2025-04-04T22:51:02Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2025-04-04T20:55:49Z" | ---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: A photo of a leaf infected with late blight disease. The leaf has
blackish brown regions of infection, hd, 4k
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- 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. -->
# DreamBooth - ButterChicken98/pv_lb_v3
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on A photo of a leaf infected with late blight disease. The leaf has blackish brown regions of infection, hd, 4k using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: True.
## 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-perplexity-correlations-410m-3-openbookqa-gs0 | Tristan | "2025-04-04T22:49:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:48:49Z" | ---
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 π€ 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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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## 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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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genki10/BERT_AugV8_k7_task1_organization_sp010_lw050_fold4 | genki10 | "2025-04-04T22:49:29Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T22:36:28Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k7_task1_organization_sp010_lw050_fold4
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. -->
# BERT_AugV8_k7_task1_organization_sp010_lw050_fold4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8173
- Qwk: 0.4170
- Mse: 0.8173
- Rmse: 0.9040
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 5 | 7.8864 | 0.0018 | 7.8864 | 2.8083 |
| No log | 2.0 | 10 | 3.9419 | 0.0050 | 3.9419 | 1.9854 |
| No log | 3.0 | 15 | 1.9986 | 0.0610 | 1.9986 | 1.4137 |
| No log | 4.0 | 20 | 1.1620 | 0.0107 | 1.1620 | 1.0779 |
| No log | 5.0 | 25 | 1.0789 | 0.0107 | 1.0789 | 1.0387 |
| No log | 6.0 | 30 | 1.1126 | 0.0107 | 1.1126 | 1.0548 |
| No log | 7.0 | 35 | 1.0903 | 0.0329 | 1.0903 | 1.0442 |
| No log | 8.0 | 40 | 0.8853 | 0.2319 | 0.8853 | 0.9409 |
| No log | 9.0 | 45 | 0.8564 | 0.3254 | 0.8564 | 0.9254 |
| No log | 10.0 | 50 | 0.9124 | 0.3623 | 0.9124 | 0.9552 |
| No log | 11.0 | 55 | 0.6780 | 0.4172 | 0.6780 | 0.8234 |
| No log | 12.0 | 60 | 0.6274 | 0.4522 | 0.6274 | 0.7921 |
| No log | 13.0 | 65 | 0.7096 | 0.4518 | 0.7096 | 0.8424 |
| No log | 14.0 | 70 | 1.1526 | 0.3578 | 1.1526 | 1.0736 |
| No log | 15.0 | 75 | 0.6934 | 0.4842 | 0.6934 | 0.8327 |
| No log | 16.0 | 80 | 0.7286 | 0.4799 | 0.7286 | 0.8536 |
| No log | 17.0 | 85 | 1.4348 | 0.2293 | 1.4348 | 1.1978 |
| No log | 18.0 | 90 | 0.9149 | 0.3874 | 0.9149 | 0.9565 |
| No log | 19.0 | 95 | 0.8750 | 0.4119 | 0.8750 | 0.9354 |
| No log | 20.0 | 100 | 0.9184 | 0.3975 | 0.9184 | 0.9584 |
| No log | 21.0 | 105 | 0.8657 | 0.3991 | 0.8657 | 0.9305 |
| No log | 22.0 | 110 | 1.0798 | 0.3396 | 1.0798 | 1.0391 |
| No log | 23.0 | 115 | 0.8053 | 0.3797 | 0.8053 | 0.8974 |
| No log | 24.0 | 120 | 0.7777 | 0.3966 | 0.7777 | 0.8819 |
| No log | 25.0 | 125 | 0.8529 | 0.4181 | 0.8529 | 0.9235 |
| No log | 26.0 | 130 | 0.8452 | 0.4042 | 0.8452 | 0.9193 |
| No log | 27.0 | 135 | 1.0081 | 0.3919 | 1.0081 | 1.0040 |
| No log | 28.0 | 140 | 1.1053 | 0.3711 | 1.1053 | 1.0513 |
| No log | 29.0 | 145 | 0.9211 | 0.4038 | 0.9211 | 0.9597 |
| No log | 30.0 | 150 | 0.8173 | 0.4170 | 0.8173 | 0.9040 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
Tristan/multilingual-1b-raw-openbookqa-gs9 | Tristan | "2025-04-04T22:48:47Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:46:57Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tristan/fasttext-160m-raw-openbookqa-gs11 | Tristan | "2025-04-04T22:46:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:46:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tristan/v2-1b-raw-openbookqa-gs7 | Tristan | "2025-04-04T22:46:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:44:34Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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[More Information Needed]
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Sonsuper228/person_in_skullpanda_style_LoRA | Sonsuper228 | "2025-04-04T22:42:14Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T22:10:43Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: person in skullpanda style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - Sonsuper228/person_in_skullpanda_style_LoRA
<Gallery />
## Model description
These are Sonsuper228/person_in_skullpanda_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use person in skullpanda style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Sonsuper228/person_in_skullpanda_style_LoRA/tree/main) them in the Files & versions tab.
## 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] |
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG-0401225210-epoch-1 | vectorzhou | "2025-04-04T22:41:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"nash-md",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2312.00886",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:38:39Z" | ---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- nash-md
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-PKU-SafeRLHF-NashMDPG-0401225210-epoch-1", 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/zhourunlongvector/nlhf/runs/13ddcrg0)
This model was trained with Nash-MD, a method introduced in [Nash Learning from Human Feedback](https://huggingface.co/papers/2312.00886).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite Nash-MD as:
```bibtex
@inproceedings{munos2024nash,
title = {Nash Learning from Human Feedback},
author = {R{'{e}}mi Munos and Michal Valko and Daniele Calandriello and Mohammad Gheshlaghi Azar and Mark Rowland and Zhaohan Daniel Guo and Yunhao Tang and Matthieu Geist and Thomas Mesnard and C{\^{o}}me Fiegel and Andrea Michi and Marco Selvi and Sertan Girgin and Nikola Momchev and Olivier Bachem and Daniel J. Mankowitz and Doina Precup and Bilal Piot},
year = 2024,
booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
publisher = {OpenReview.net},
url = {https://openreview.net/forum?id=Y5AmNYiyCQ}
}
```
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 GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/v2-160m-raw-openbookqa-gs6 | Tristan | "2025-04-04T22:40:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:40:16Z" | ---
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 π€ 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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Tristan/dclm-id-410m-raw-openbookqa-gs6 | Tristan | "2025-04-04T22:40:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:39:25Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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tinycompany/Qwentify-qtk-Knight-3B | tinycompany | "2025-04-04T22:40:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:34:47Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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DreadPoor/Testy2 | DreadPoor | "2025-04-04T22:40:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Elizezen/Himeyuri-v0.1-12B",
"base_model:merge:Elizezen/Himeyuri-v0.1-12B",
"base_model:Epiculous/Crimson_Dawn-v0.2",
"base_model:merge:Epiculous/Crimson_Dawn-v0.2",
"base_model:LatitudeGames/Wayfarer-12B",
"base_model:merge:LatitudeGames/Wayfarer-12B",
"base_model:NeverSleep/Lumimaid-v0.2-12B",
"base_model:merge:NeverSleep/Lumimaid-v0.2-12B",
"base_model:TheDrummer/UnslopNemo-12B-v4",
"base_model:merge:TheDrummer/UnslopNemo-12B-v4",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:34:01Z" | ---
base_model:
- Epiculous/Crimson_Dawn-v0.2
- TheDrummer/UnslopNemo-12B-v4
- LatitudeGames/Wayfarer-12B
- NeverSleep/Lumimaid-v0.2-12B
- Elizezen/Himeyuri-v0.1-12B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [TheDrummer/UnslopNemo-12B-v4](https://huggingface.co/TheDrummer/UnslopNemo-12B-v4) as a base.
### Models Merged
The following models were included in the merge:
* [Epiculous/Crimson_Dawn-v0.2](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2)
* [LatitudeGames/Wayfarer-12B](https://huggingface.co/LatitudeGames/Wayfarer-12B)
* [NeverSleep/Lumimaid-v0.2-12B](https://huggingface.co/NeverSleep/Lumimaid-v0.2-12B)
* [Elizezen/Himeyuri-v0.1-12B](https://huggingface.co/Elizezen/Himeyuri-v0.1-12B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Epiculous/Crimson_Dawn-v0.2
- model: Elizezen/Himeyuri-v0.1-12B
- model: NeverSleep/Lumimaid-v0.2-12B
- model: LatitudeGames/Wayfarer-12B
merge_method: model_stock
base_model: TheDrummer/UnslopNemo-12B-v4
normalize: false
int8_mask: true
dtype: bfloat16
```
|
Tristan/dclm-id-410m-raw-openbookqa-gs9 | Tristan | "2025-04-04T22:39:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:38:38Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Tristan/v2-410m-raw-openbookqa-gs0 | Tristan | "2025-04-04T22:37:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:36:42Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Tristan/dclm-sampled-labels-1b-openbookqa-gs4 | Tristan | "2025-04-04T22:36:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:34:41Z" | ---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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genki10/BERT_AugV8_k7_task1_organization_sp010_lw050_fold3 | genki10 | "2025-04-04T22:36:20Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T22:19:55Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k7_task1_organization_sp010_lw050_fold3
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. -->
# BERT_AugV8_k7_task1_organization_sp010_lw050_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7351
- Qwk: 0.4482
- Mse: 0.7351
- Rmse: 0.8574
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 1.0 | 5 | 8.7558 | 0.0 | 8.7544 | 2.9588 |
| No log | 2.0 | 10 | 4.0856 | 0.0147 | 4.0847 | 2.0211 |
| No log | 3.0 | 15 | 2.5583 | -0.0044 | 2.5568 | 1.5990 |
| No log | 4.0 | 20 | 1.3074 | 0.0365 | 1.3067 | 1.1431 |
| No log | 5.0 | 25 | 0.9132 | 0.2344 | 0.9127 | 0.9554 |
| No log | 6.0 | 30 | 1.2870 | 0.0202 | 1.2864 | 1.1342 |
| No log | 7.0 | 35 | 0.9368 | 0.1397 | 0.9363 | 0.9676 |
| No log | 8.0 | 40 | 0.8373 | 0.3241 | 0.8369 | 0.9148 |
| No log | 9.0 | 45 | 0.8207 | 0.2956 | 0.8209 | 0.9060 |
| No log | 10.0 | 50 | 0.7779 | 0.2986 | 0.7780 | 0.8820 |
| No log | 11.0 | 55 | 0.7993 | 0.2742 | 0.7997 | 0.8943 |
| No log | 12.0 | 60 | 0.8118 | 0.2802 | 0.8122 | 0.9012 |
| No log | 13.0 | 65 | 0.8019 | 0.3491 | 0.8022 | 0.8957 |
| No log | 14.0 | 70 | 0.9175 | 0.2937 | 0.9172 | 0.9577 |
| No log | 15.0 | 75 | 0.9732 | 0.2145 | 0.9727 | 0.9862 |
| No log | 16.0 | 80 | 0.7431 | 0.4111 | 0.7430 | 0.8620 |
| No log | 17.0 | 85 | 0.7011 | 0.4360 | 0.7009 | 0.8372 |
| No log | 18.0 | 90 | 0.7784 | 0.3936 | 0.7782 | 0.8821 |
| No log | 19.0 | 95 | 0.9530 | 0.3660 | 0.9525 | 0.9759 |
| No log | 20.0 | 100 | 0.8369 | 0.3601 | 0.8365 | 0.9146 |
| No log | 21.0 | 105 | 1.0994 | 0.2883 | 1.0986 | 1.0481 |
| No log | 22.0 | 110 | 0.8748 | 0.3917 | 0.8749 | 0.9353 |
| No log | 23.0 | 115 | 0.6871 | 0.5058 | 0.6873 | 0.8290 |
| No log | 24.0 | 120 | 1.1947 | 0.1520 | 1.1938 | 1.0926 |
| No log | 25.0 | 125 | 0.6360 | 0.5001 | 0.6361 | 0.7976 |
| No log | 26.0 | 130 | 0.8399 | 0.3798 | 0.8397 | 0.9164 |
| No log | 27.0 | 135 | 1.0174 | 0.2208 | 1.0169 | 1.0084 |
| No log | 28.0 | 140 | 0.8299 | 0.3764 | 0.8299 | 0.9110 |
| No log | 29.0 | 145 | 0.7283 | 0.4416 | 0.7284 | 0.8535 |
| No log | 30.0 | 150 | 0.9780 | 0.2721 | 0.9777 | 0.9888 |
| No log | 31.0 | 155 | 1.1864 | 0.1681 | 1.1859 | 1.0890 |
| No log | 32.0 | 160 | 1.0659 | 0.1954 | 1.0656 | 1.0323 |
| No log | 33.0 | 165 | 0.7851 | 0.4260 | 0.7851 | 0.8860 |
| No log | 34.0 | 170 | 1.0701 | 0.2268 | 1.0697 | 1.0342 |
| No log | 35.0 | 175 | 0.8263 | 0.3845 | 0.8261 | 0.9089 |
| No log | 36.0 | 180 | 0.7174 | 0.4480 | 0.7175 | 0.8470 |
| No log | 37.0 | 185 | 1.1491 | 0.2055 | 1.1485 | 1.0717 |
| No log | 38.0 | 190 | 0.7351 | 0.4482 | 0.7351 | 0.8574 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
Tristan/dclm-random-160m-openbookqa-gs6 | Tristan | "2025-04-04T22:34:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:34:16Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf | RichardErkhov | "2025-04-04T22:32:49Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T21:52:27Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
dazzle_3B_merged - GGUF
- Model creator: https://huggingface.co/Jeffsimpsons/
- Original model: https://huggingface.co/Jeffsimpsons/dazzle_3B_merged/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [dazzle_3B_merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q2_K.gguf) | Q2_K | 1.27GB |
| [dazzle_3B_merged.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [dazzle_3B_merged.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [dazzle_3B_merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [dazzle_3B_merged.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [dazzle_3B_merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q3_K.gguf) | Q3_K | 1.57GB |
| [dazzle_3B_merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [dazzle_3B_merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [dazzle_3B_merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [dazzle_3B_merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q4_0.gguf) | Q4_0 | 1.79GB |
| [dazzle_3B_merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [dazzle_3B_merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [dazzle_3B_merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q4_K.gguf) | Q4_K | 1.88GB |
| [dazzle_3B_merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [dazzle_3B_merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q4_1.gguf) | Q4_1 | 1.95GB |
| [dazzle_3B_merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q5_0.gguf) | Q5_0 | 2.11GB |
| [dazzle_3B_merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [dazzle_3B_merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q5_K.gguf) | Q5_K | 2.16GB |
| [dazzle_3B_merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [dazzle_3B_merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q5_1.gguf) | Q5_1 | 2.28GB |
| [dazzle_3B_merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.Q6_K.gguf) | Q6_K | 2.46GB |
| [dazzle_3B_merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/Jeffsimpsons_-_dazzle_3B_merged-gguf/blob/main/dazzle_3B_merged.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 π€ 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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<!-- 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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<!-- 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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|
Tristan/dclm-sampled-labels-1b-openbookqa-gs11 | Tristan | "2025-04-04T22:32:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:30:37Z" | ---
library_name: transformers
tags: []
---
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shrenikb/spectral_soft_clustered_only_diff | shrenikb | "2025-04-04T22:30:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:27:03Z" | ---
library_name: transformers
tags: []
---
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Tristan/dclm-id-1b-raw-openbookqa-gs3 | Tristan | "2025-04-04T22:30:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:28:46Z" | ---
library_name: transformers
tags: []
---
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TrainUnstudio/fused-ecomstyle-a0.6-l0.7 | TrainUnstudio | "2025-04-04T22:29:26Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
] | text-to-image | "2025-04-04T22:19:44Z" | ---
library_name: diffusers
---
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oncu/Turkish-Llama-3-8B-function-calling-GGUF | oncu | "2025-04-04T22:28:35Z" | 1 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"text-generation",
"en",
"tr",
"dataset:atasoglu/turkish-function-calling-20k",
"base_model:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"base_model:quantized:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-03-31T17:12:47Z" | ---
base_model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
- tr
datasets:
- atasoglu/turkish-function-calling-20k
pipeline_tag: text-generation
---
# Uploaded model
**This model was adapted from [ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1](https://huggingface.co/ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1) and fine-tuned on the [atasoglu/turkish-function-calling-20k](https://huggingface.co/datasets/atasoglu/turkish-function-calling-20k) dataset to perform function calling tasks in Turkish.**
- **Developed by:** atasoglu
- **License:** apache-2.0
- **Finetuned from model :** ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
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)
# Usage
First, load the model:
```python
import json
from unsloth import FastLanguageModel
# loading the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="atasoglu/Turkish-Llama-3-8B-function-calling",
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
```
Setup the tools and messages:
```python
# define the prompt templates
system_prompt = """Sen yardΔ±msever, akΔ±llΔ± ve fonksiyon Γ§aΔrΔ±sΔ± yapabilen bir asistansΔ±n.
AΕaΔΔ±da JSON parΓ§asΔ± iΓ§inde verilen fonksiyonlarΔ± kullanarak kullanΔ±cΔ±nΔ±n sorusunu uygun Εekilde cevaplamanΔ± istiyorum.
Fonksiyon Γ§aΔrΔ±sΔ± yaparken uyman gereken talimatlar:
* Fonksiyonlar, JSON ΕemasΔ± olarak ifade edilmiΕtir.
* EΔer kullanΔ±cΔ±nΔ±n sorusu, bu fonksiyonlardan en az biri kullanΔ±larak cevaplanabiliyorsa; uygun bir fonksiyon Γ§aΔrΔ±sΔ±nΔ± JSON parΓ§asΔ± iΓ§inde oluΕtur.
* FonksiyonlarΔ±n parametreleri iΓ§in asla uydurmalar yapma ve sadece kullanΔ±cΔ±nΔ±n verdiΔi bilgileri kullan.
* EΔer kullanΔ±cΔ±nΔ±n sorusu herhangi bir fonksiyon ile cevaplanamΔ±yorsa, sadece "Verilen fonksiyonlarla cevaplanamaz" metnini dΓΆndΓΌr ve baΕka bir aΓ§Δ±klama yapma.
Bu talimatlara uyarak sorularΔ± cevaplandΔ±r."""
user_prompt = """### Fonksiyonlar
'''json
{tools}
'''
### Soru
{query}"""
# define the tools and messages
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. BogotΓ‘, Colombia",
}
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
}
]
query = "Paris'te hava Εu anda nasΔ±l?"
messages = [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": user_prompt.format(
tools=json.dumps(tools, ensure_ascii=False),
query=query,
),
},
]
```
**NOTE:** Change the *single quote* character to a *backtick* in the user prompt before running to specify the JSON snippet.
Then, generate and evaluate the output:
```python
import re
# define an evaluation function
def eval_function_calling(text):
match_ = re.search(r"```json(.*)```", text, re.DOTALL)
if match_ is None:
return False, text
return True, json.loads(match_.group(1).strip())
# tokenize the inputs
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
).to("cuda")
# define generation arguments
generation_kwargs = dict(
do_sample=True,
use_cache=True,
max_new_tokens=500,
temperature=0.3,
top_p=0.9,
top_k=40,
)
# finally, generate the output
outputs = model.generate(**inputs, **generation_kwargs)
output_ids = outputs[:, inputs["input_ids"].shape[1] :]
generated_texts = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
has_function_calling, results = eval_function_calling(generated_texts[0])
# print the model response
if has_function_calling:
for result in results:
fn = result["function"]
name, args = fn["name"], fn["arguments"]
print(f"Calling {name!r} function with these arguments: {args}")
else:
print(f"No function call: {results!r}")
```
Output:
```console
Calling 'get_weather' function with these arguments: {"location":"Paris, France"}
``` |
canertugrul/DeepSeek-R1-Distill-Qwen-14B-Tool-Use-Tokenizer_v2 | canertugrul | "2025-04-04T22:28:15Z" | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T22:28:13Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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## Model Details
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canertugrul/DeepSeek-R1-Distill-Qwen-14B-Tool-Use-Adapter_v2 | canertugrul | "2025-04-04T22:28:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T22:25:25Z" | ---
base_model: unsloth/deepseek-r1-distill-qwen-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** canertugrul
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-14b-unsloth-bnb-4bit
This qwen2 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)
|
tanuuu/distilbert-security-v2 | tanuuu | "2025-04-04T22:28:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T22:27:49Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf | RichardErkhov | "2025-04-04T22:27:57Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T22:23:54Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gita-text-generation-gpt2 - GGUF
- Model creator: https://huggingface.co/Vidyayini/
- Original model: https://huggingface.co/Vidyayini/gita-text-generation-gpt2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gita-text-generation-gpt2.Q2_K.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q2_K.gguf) | Q2_K | 0.08GB |
| [gita-text-generation-gpt2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.IQ3_XS.gguf) | IQ3_XS | 0.08GB |
| [gita-text-generation-gpt2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.IQ3_S.gguf) | IQ3_S | 0.08GB |
| [gita-text-generation-gpt2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q3_K_S.gguf) | Q3_K_S | 0.08GB |
| [gita-text-generation-gpt2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.IQ3_M.gguf) | IQ3_M | 0.09GB |
| [gita-text-generation-gpt2.Q3_K.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q3_K.gguf) | Q3_K | 0.09GB |
| [gita-text-generation-gpt2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q3_K_M.gguf) | Q3_K_M | 0.09GB |
| [gita-text-generation-gpt2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q3_K_L.gguf) | Q3_K_L | 0.1GB |
| [gita-text-generation-gpt2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.IQ4_XS.gguf) | IQ4_XS | 0.1GB |
| [gita-text-generation-gpt2.Q4_0.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q4_0.gguf) | Q4_0 | 0.1GB |
| [gita-text-generation-gpt2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.IQ4_NL.gguf) | IQ4_NL | 0.1GB |
| [gita-text-generation-gpt2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q4_K_S.gguf) | Q4_K_S | 0.1GB |
| [gita-text-generation-gpt2.Q4_K.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q4_K.gguf) | Q4_K | 0.11GB |
| [gita-text-generation-gpt2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q4_K_M.gguf) | Q4_K_M | 0.11GB |
| [gita-text-generation-gpt2.Q4_1.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q4_1.gguf) | Q4_1 | 0.11GB |
| [gita-text-generation-gpt2.Q5_0.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q5_0.gguf) | Q5_0 | 0.11GB |
| [gita-text-generation-gpt2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q5_K_S.gguf) | Q5_K_S | 0.11GB |
| [gita-text-generation-gpt2.Q5_K.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q5_K.gguf) | Q5_K | 0.12GB |
| [gita-text-generation-gpt2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q5_K_M.gguf) | Q5_K_M | 0.12GB |
| [gita-text-generation-gpt2.Q5_1.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q5_1.gguf) | Q5_1 | 0.12GB |
| [gita-text-generation-gpt2.Q6_K.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q6_K.gguf) | Q6_K | 0.13GB |
| [gita-text-generation-gpt2.Q8_0.gguf](https://huggingface.co/RichardErkhov/Vidyayini_-_gita-text-generation-gpt2-gguf/blob/main/gita-text-generation-gpt2.Q8_0.gguf) | Q8_0 | 0.17GB |
Original model description:
---
library_name: transformers
tags: []
---
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|
curfy/blip2-opt-2.7b-SWG-MORPH-captions-adapters | curfy | "2025-04-04T22:27:16Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T20:20:50Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed] |
Tristan/dclm-random-410m-openbookqa-gs2 | Tristan | "2025-04-04T22:26:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:25:58Z" | ---
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 π€ 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/dclm-sampled-labels-410m-openbookqa-gs3 | Tristan | "2025-04-04T22:24:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:23:10Z" | ---
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 π€ 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] |
immad123/deepseek-psych-llm | immad123 | "2025-04-04T22:23:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2025-04-04T22:22:50Z" | ---
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:** immad123
- **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)
|
LeeDavee/DAVE | LeeDavee | "2025-04-04T22:22:58Z" | 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 | "2025-04-04T21:59:19Z" | ---
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: DAVE
---
# Dave
<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 `DAVE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "DAVE",
"lora_weights": "https://huggingface.co/LeeDavee/DAVE/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 [𧨠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('LeeDavee/DAVE', weight_name='lora.safetensors')
image = pipeline('DAVE').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/LeeDavee/DAVE/discussions) to add images that show off what youβve made with this LoRA.
|
thirdeyeai/elevate360m-orca | thirdeyeai | "2025-04-04T22:22:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"chat",
"tool-calling",
"instruction-tuned",
"edge-device",
"conversational",
"dataset:Open-Orca/OpenOrca",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T13:20:10Z" | ---
library_name: transformers
tags:
- chat
- tool-calling
- instruction-tuned
- edge-device
- conversational
license: mit
datasets:
- Open-Orca/OpenOrca
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
---
# Model Card for `thirdeyeai/elevate-360m`
## Model Summary
360M parameter transformer model trained for efficient chat completion and tool call prediction on edge devices. Suitable for low-latency applications.
## Model Details
- **Developed by:** Thirdeye AI
- **Finetuned from model:** HuggingFaceTB/SmolLM2-360M-Instruct
- **Model type:** Causal decoder-only transformer
- **Language(s):** English
- **License:** apache-2.0
- **Hardware:** Trained on 1x A100 GPU
- **Training time:** < 24 hours
## Model Sources
- **Repository:** https://huggingface.co/thirdeyeai/elevate-360m
## Uses
### Direct Use
Primarily for chat completion and tool call prediction in edge environments with constrained resources.
### Out-of-Scope Use
Not optimized for multi-language support, long-context reasoning, or open-ended generation without tool grounding.
## Bias, Risks, and Limitations
Trained on publicly available instruction-following datasets. May reflect biases present in those datasets. Not suitable for high-stakes or safety-critical applications.
### Recommendations
Use only with proper evaluation and safety checks in deployment environments. Validate outputs before taking action. |
TigerAQ/RLppo-LunarLander-v3 | TigerAQ | "2025-04-04T22:19:10Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-04-04T22:18:54Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v3
type: LunarLander-v3
metrics:
- type: mean_reward
value: 265.24 +/- 20.03
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v3**
This is a trained model of a **PPO** agent playing **LunarLander-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
...
```
|
MinaMila/phi3_unlearned_Adult_11ep_33 | MinaMila | "2025-04-04T22:18:16Z" | 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 | "2025-04-04T22:15:53Z" | ---
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)
|
lesso17/92fb6986-68d7-4140-9332-6e6b1bb2ddbc | lesso17 | "2025-04-04T22:18:13Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-1.5B",
"base_model:adapter:unsloth/Qwen2-1.5B",
"license:apache-2.0",
"region:us"
] | null | "2025-04-04T21:10:38Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 92fb6986-68d7-4140-9332-6e6b1bb2ddbc
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/Qwen2-1.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c429e1f5fafc4ea0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c429e1f5fafc4ea0_train_data.json
type:
field_input: id
field_instruction: url
field_output: response
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: lesso17/92fb6986-68d7-4140-9332-6e6b1bb2ddbc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000217
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/c429e1f5fafc4ea0_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: 170
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: 881c3d6e-8dfd-421b-aec3-7ef632f40f3b
wandb_project: 17a
wandb_run: your_name
wandb_runid: 881c3d6e-8dfd-421b-aec3-7ef632f40f3b
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 92fb6986-68d7-4140-9332-6e6b1bb2ddbc
This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8522
## 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: 0.000217
- train_batch_size: 4
- eval_batch_size: 4
- seed: 170
- 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 | 0.0001 | 1 | 2.3355 |
| 1.8957 | 0.0702 | 500 | 1.8522 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
FEEYan/poker_Qwen2.5_3B_instruct_grpo | FEEYan | "2025-04-04T22:15:34Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | "2025-04-04T22:02:22Z" | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-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. -->
- **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
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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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. -->
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<!-- Relevant interpretability work for the model goes here -->
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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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## Technical Specifications [optional]
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[More Information Needed]
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### Framework versions
- PEFT 0.15.1 |
tanuuu/distilbert-scalability-v1 | tanuuu | "2025-04-04T22:15:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T22:14:54Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
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[More Information Needed]
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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
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[More Information Needed]
## Training Details
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ROYERBIN1/SONIC_FILE | ROYERBIN1 | "2025-04-04T22:14:28Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-03-31T21:14:32Z" | ---
license: apache-2.0
---
|
Tristan/dclm-perplexity-correlations-160m-3-openbookqa-gs3 | Tristan | "2025-04-04T22:14:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:14:02Z" | ---
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 π€ 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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### Model Sources [optional]
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[More Information Needed]
### Out-of-Scope Use
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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
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[More Information Needed]
## Training Details
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### Testing Data, Factors & Metrics
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[More Information Needed]
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### Results
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#### 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 -->
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- **Hardware Type:** [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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[More Information Needed] |
YungRox/Lacazette2 | YungRox | "2025-04-04T22:13:02Z" | 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 | "2025-04-04T21:46:17Z" | ---
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: Lacazette
---
# Lacazette2
<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 `Lacazette` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Lacazette",
"lora_weights": "https://huggingface.co/YungRox/Lacazette2/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 [𧨠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('YungRox/Lacazette2', weight_name='lora.safetensors')
image = pipeline('Lacazette').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: 25
## Contribute your own examples
You can use the [community tab](https://huggingface.co/YungRox/Lacazette2/discussions) to add images that show off what youβve made with this LoRA.
|
qdwqwd/sammanta | qdwqwd | "2025-04-04T22:11:10Z" | 0 | 0 | null | [
"safetensors",
"unsloth",
"license:mit",
"region:us"
] | null | "2025-04-04T21:37:37Z" | ---
license: mit
tags:
- unsloth
---
|
fedorl/ppo-PyramidTraining | fedorl | "2025-04-04T22:09:22Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | "2025-04-04T22:06:02Z" | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog πΆ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: fedorl/ppo-PyramidTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
genki10/BERT_AugV8_k7_task1_organization_sp010_lw050_fold1 | genki10 | "2025-04-04T22:09:06Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T21:57:30Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k7_task1_organization_sp010_lw050_fold1
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. -->
# BERT_AugV8_k7_task1_organization_sp010_lw050_fold1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9599
- Qwk: 0.2562
- Mse: 0.9583
- Rmse: 0.9789
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 1.0 | 5 | 9.2500 | 0.0 | 9.2475 | 3.0410 |
| No log | 2.0 | 10 | 5.4513 | 0.0404 | 5.4492 | 2.3344 |
| No log | 3.0 | 15 | 3.2322 | 0.0 | 3.2303 | 1.7973 |
| No log | 4.0 | 20 | 1.7113 | 0.0211 | 1.7097 | 1.3076 |
| No log | 5.0 | 25 | 1.0606 | 0.0 | 1.0591 | 1.0291 |
| No log | 6.0 | 30 | 1.0008 | -0.0488 | 0.9993 | 0.9996 |
| No log | 7.0 | 35 | 1.1676 | 0.1219 | 1.1657 | 1.0797 |
| No log | 8.0 | 40 | 0.8094 | 0.3010 | 0.8077 | 0.8987 |
| No log | 9.0 | 45 | 0.9247 | 0.2240 | 0.9234 | 0.9609 |
| No log | 10.0 | 50 | 0.8733 | 0.2712 | 0.8718 | 0.9337 |
| No log | 11.0 | 55 | 0.7908 | 0.4297 | 0.7893 | 0.8884 |
| No log | 12.0 | 60 | 1.1192 | 0.2811 | 1.1172 | 1.0570 |
| No log | 13.0 | 65 | 0.9951 | 0.3183 | 0.9932 | 0.9966 |
| No log | 14.0 | 70 | 0.9339 | 0.3508 | 0.9320 | 0.9654 |
| No log | 15.0 | 75 | 1.0149 | 0.2988 | 1.0131 | 1.0065 |
| No log | 16.0 | 80 | 1.0048 | 0.3194 | 1.0033 | 1.0016 |
| No log | 17.0 | 85 | 1.0066 | 0.2698 | 1.0050 | 1.0025 |
| No log | 18.0 | 90 | 1.4612 | 0.2286 | 1.4594 | 1.2081 |
| No log | 19.0 | 95 | 0.8734 | 0.3644 | 0.8719 | 0.9338 |
| No log | 20.0 | 100 | 1.2049 | 0.2401 | 1.2030 | 1.0968 |
| No log | 21.0 | 105 | 1.3094 | 0.1983 | 1.3074 | 1.1434 |
| No log | 22.0 | 110 | 0.9830 | 0.2361 | 0.9814 | 0.9907 |
| No log | 23.0 | 115 | 1.2641 | 0.2155 | 1.2623 | 1.1235 |
| No log | 24.0 | 120 | 0.9324 | 0.2895 | 0.9308 | 0.9648 |
| No log | 25.0 | 125 | 1.2634 | 0.2683 | 1.2615 | 1.1231 |
| No log | 26.0 | 130 | 0.9599 | 0.2562 | 0.9583 | 0.9789 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
Tristan/dclm-sampled-labels-1b-openbookqa-gs9 | Tristan | "2025-04-04T22:07:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:05:22Z" | ---
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 π€ 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]
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cst7/dog6_flux_lora_500_style | cst7 | "2025-04-04T22:06:49Z" | 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 | "2025-04-04T21:52:40Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks dog
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/dog6_flux_lora_500_style
<Gallery />
## Model description
These are cst7/dog6_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 dog` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/dog6_flux_lora_500_style/tree/main) in the Files & versions tab.
## Use it with the [𧨠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/dog6_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks dog').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] |
RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf | RichardErkhov | "2025-04-04T22:06:35Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T21:27:53Z" | 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-Instruct-ConvFinQA-1e - GGUF
- Model creator: https://huggingface.co/maxfrax/
- Original model: https://huggingface.co/maxfrax/Llama-3.2-3B-Instruct-ConvFinQA-1e/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q2_K.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q2_K.gguf) | Q2_K | 1.27GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_S.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_M.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K.gguf) | Q3_K | 1.57GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_0.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K.gguf) | Q4_K | 1.88GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_1.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_0.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K.gguf) | Q5_K | 2.16GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_1.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q6_K.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.Q6_K.gguf) | Q6_K | 2.46GB |
| [Llama-3.2-3B-Instruct-ConvFinQA-1e.Q8_0.gguf](https://huggingface.co/RichardErkhov/maxfrax_-_Llama-3.2-3B-Instruct-ConvFinQA-1e-gguf/blob/main/Llama-3.2-3B-Instruct-ConvFinQA-1e.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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|
Tristan/dclm-random-160m-openbookqa-gs5 | Tristan | "2025-04-04T22:05:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:04:55Z" | ---
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 π€ 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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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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[More Information Needed]
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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 -->
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Tristan/dclm-random-160m-openbookqa-gs0 | Tristan | "2025-04-04T22:02:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:02:20Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
## Training Details
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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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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L134L134/vewn_style_LoRA | L134L134 | "2025-04-04T22:02:01Z" | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T17:13:31Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: Art in vewn style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - L134L134/vewn_style_LoRA
<Gallery />
## Model description
These are L134L134/vewn_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use Art in vewn style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](L134L134/vewn_style_LoRA/tree/main) them in the Files & versions tab.
## 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-random-160m-raw-openbookqa-gs6 | Tristan | "2025-04-04T22:01:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T22:01:07Z" | ---
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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[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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Tristan/v2-1b-raw-openbookqa-gs5 | Tristan | "2025-04-04T22:01:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:59:06Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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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<!-- 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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Tristan/dclm-random-1b-raw-openbookqa-gs10 | Tristan | "2025-04-04T21:59:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:57:08Z" | ---
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. -->
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## How to Get Started with the Model
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[More Information Needed]
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notshivain1/albert-base-v1-stackoverflow-prediction | notshivain1 | "2025-04-04T21:57:52Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-03T20:51:15Z" | ---
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 π€ 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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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
### 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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### 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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genki10/BERT_AugV8_k7_task1_organization_sp010_lw050_fold0 | genki10 | "2025-04-04T21:57:23Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T21:44:25Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k7_task1_organization_sp010_lw050_fold0
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. -->
# BERT_AugV8_k7_task1_organization_sp010_lw050_fold0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8866
- Qwk: 0.2703
- Mse: 0.8866
- Rmse: 0.9416
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 5 | 7.0828 | 0.0 | 7.0828 | 2.6614 |
| No log | 2.0 | 10 | 5.0337 | 0.0115 | 5.0337 | 2.2436 |
| No log | 3.0 | 15 | 2.9358 | 0.0 | 2.9358 | 1.7134 |
| No log | 4.0 | 20 | 1.4807 | 0.0316 | 1.4807 | 1.2168 |
| No log | 5.0 | 25 | 1.0849 | 0.0212 | 1.0849 | 1.0416 |
| No log | 6.0 | 30 | 0.8993 | 0.0906 | 0.8993 | 0.9483 |
| No log | 7.0 | 35 | 0.7771 | 0.3205 | 0.7771 | 0.8815 |
| No log | 8.0 | 40 | 0.6844 | 0.3230 | 0.6844 | 0.8273 |
| No log | 9.0 | 45 | 0.6430 | 0.3243 | 0.6430 | 0.8019 |
| No log | 10.0 | 50 | 0.6378 | 0.3431 | 0.6378 | 0.7986 |
| No log | 11.0 | 55 | 0.6941 | 0.2945 | 0.6941 | 0.8331 |
| No log | 12.0 | 60 | 0.6694 | 0.3734 | 0.6694 | 0.8182 |
| No log | 13.0 | 65 | 0.8616 | 0.3763 | 0.8616 | 0.9282 |
| No log | 14.0 | 70 | 0.6775 | 0.4313 | 0.6775 | 0.8231 |
| No log | 15.0 | 75 | 0.5692 | 0.4919 | 0.5692 | 0.7545 |
| No log | 16.0 | 80 | 0.9649 | 0.2756 | 0.9649 | 0.9823 |
| No log | 17.0 | 85 | 0.7177 | 0.4445 | 0.7177 | 0.8472 |
| No log | 18.0 | 90 | 0.6553 | 0.4662 | 0.6553 | 0.8095 |
| No log | 19.0 | 95 | 0.8372 | 0.3841 | 0.8372 | 0.9150 |
| No log | 20.0 | 100 | 0.6321 | 0.4500 | 0.6321 | 0.7951 |
| No log | 21.0 | 105 | 0.7812 | 0.3672 | 0.7812 | 0.8839 |
| No log | 22.0 | 110 | 0.7247 | 0.4006 | 0.7247 | 0.8513 |
| No log | 23.0 | 115 | 0.7236 | 0.3798 | 0.7236 | 0.8506 |
| No log | 24.0 | 120 | 0.8148 | 0.3489 | 0.8148 | 0.9027 |
| No log | 25.0 | 125 | 0.7721 | 0.4267 | 0.7721 | 0.8787 |
| No log | 26.0 | 130 | 0.7260 | 0.4062 | 0.7260 | 0.8520 |
| No log | 27.0 | 135 | 0.7200 | 0.4153 | 0.7200 | 0.8485 |
| No log | 28.0 | 140 | 0.7049 | 0.4484 | 0.7049 | 0.8396 |
| No log | 29.0 | 145 | 0.6856 | 0.4558 | 0.6856 | 0.8280 |
| No log | 30.0 | 150 | 0.8866 | 0.2703 | 0.8866 | 0.9416 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
TrainUnstudio/fused-ecomstyle | TrainUnstudio | "2025-04-04T21:56:25Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
] | text-to-image | "2025-04-04T21:45:07Z" | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 𧨠diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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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]
### 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
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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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#### 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]
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- **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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jahyungu/Qwen2.5-Math-7B-Instruct_ocg | jahyungu | "2025-04-04T21:52:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T09:12:35Z" | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Math-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen2.5-Math-7B-Instruct_ocg
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Qwen2.5-Math-7B-Instruct_ocg
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
Tristan/v2-160m-raw-openbookqa-gs7 | Tristan | "2025-04-04T21:52:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:52:17Z" | ---
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 π€ 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
pictgensupport/farmstand | pictgensupport | "2025-04-04T21:52:20Z" | 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 | "2025-04-04T21:52:17Z" | ---
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: farmstand
---
# Farmstand
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `farmstand` to trigger the image generation.
## Use it with the [𧨠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('pictgensupport/farmstand', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
shehryaraijaz/marianmt-legal-translation-en-ur | shehryaraijaz | "2025-04-04T21:51:05Z" | 0 | 0 | null | [
"safetensors",
"marian",
"region:us"
] | null | "2025-04-04T19:54:26Z" | # π§Ύ MarianMT Legal Translation (English β Urdu)
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ur](https://huggingface.co/Helsinki-NLP/opus-mt-en-ur) on the [`nlp-anonymous-researcher/LEGAL-UQA`](https://huggingface.co/datasets/nlp-anonymous-researcher/LEGAL-UQA) dataset. It is specifically adapted for translating **legal English** into **Urdu** for use in tasks such as question answering, document review, and cross-lingual legal understanding.
---
## π Model Details
- **Base model**: [Helsinki-NLP/opus-mt-en-ur](https://huggingface.co/Helsinki-NLP/opus-mt-en-ur)
- **Fine-tuned on**: [LEGAL-UQA dataset](https://huggingface.co/datasets/nlp-anonymous-researcher/LEGAL-UQA)
- **Language pair**: English β Urdu
- **Domain**: Legal documents and question answering
- **Model type**: MarianMT (transformer-based neural machine translation)
---
## π Dataset
- **Dataset**: [`nlp-anonymous-researcher/LEGAL-UQA`](https://huggingface.co/datasets/nlp-anonymous-researcher/LEGAL-UQA)
- **Task**: Legal question answering in both English and Urdu.
- **Structure**:
- `question_eng`, `context_eng`, `answer_eng`
- `question_urdu`, `context_urdu`, `answer_urdu`
---
## π Evaluation
### π Training Logs (15 Epochs)
```
Epoch Training Loss Validation Loss BLEU
1 0.763500 0.667774 8.057221
2 0.476500 0.555881 12.368178
3 0.497900 0.494743 16.143960
4 0.370900 0.447808 17.798097
5 0.397500 0.410777 20.325816
6 0.295500 0.381411 21.924673
7 0.210900 0.356772 23.402798
8 0.342000 0.336495 25.737743
9 0.246200 0.318600 26.673531
10 0.182800 0.304384 27.381958
11 0.277100 0.294487 28.791597
12 0.230500 0.286031 29.388235
13 0.231500 0.278773 30.365457
14 0.169500 0.274858 31.054984
15 0.203700 0.273266 31.120550
```
---
## π Usage
```python
from transformers import MarianMTModel, MarianTokenizer
model_name = "shehryaraijaz/marianmt-legal-translation-en-ur"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
text = "What are the requirements for a valid contract under Pakistani law?"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
translated = model.generate(**inputs)
print(tokenizer.decode(translated[0], skip_special_tokens=True))
```
---
## π‘ Intended Use
This model is best suited for:
- Translating legal documents from English to Urdu
- Assisting bilingual legal research
- Supporting legal professionals in multilingual environments
---
## β οΈ Limitations
- Not suitable for informal or general-purpose translation.
- The model may not fully capture regional legal terminology or variations in legal Urdu.
---
## π License
This model is distributed under the same license as the base model ([OPUS license](http://opus.nlpl.eu/)). Please refer to the datasetβs license for usage of LEGAL-UQA.
|
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf | RichardErkhov | "2025-04-04T21:50:30Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T20:46:59Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
GPT2XL_RLLMv15-1 - GGUF
- Model creator: https://huggingface.co/migueldeguzmandev/
- Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv15-1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [GPT2XL_RLLMv15-1.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q2_K.gguf) | Q2_K | 0.84GB |
| [GPT2XL_RLLMv15-1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.IQ3_XS.gguf) | IQ3_XS | 0.84GB |
| [GPT2XL_RLLMv15-1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.IQ3_S.gguf) | IQ3_S | 0.84GB |
| [GPT2XL_RLLMv15-1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q3_K_S.gguf) | Q3_K_S | 0.84GB |
| [GPT2XL_RLLMv15-1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.IQ3_M.gguf) | IQ3_M | 0.91GB |
| [GPT2XL_RLLMv15-1.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q3_K.gguf) | Q3_K | 0.97GB |
| [GPT2XL_RLLMv15-1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q3_K_M.gguf) | Q3_K_M | 0.97GB |
| [GPT2XL_RLLMv15-1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q3_K_L.gguf) | Q3_K_L | 1.03GB |
| [GPT2XL_RLLMv15-1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.IQ4_XS.gguf) | IQ4_XS | 0.9GB |
| [GPT2XL_RLLMv15-1.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q4_0.gguf) | Q4_0 | 0.91GB |
| [GPT2XL_RLLMv15-1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.IQ4_NL.gguf) | IQ4_NL | 0.91GB |
| [GPT2XL_RLLMv15-1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q4_K_S.gguf) | Q4_K_S | 1.04GB |
| [GPT2XL_RLLMv15-1.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q4_K.gguf) | Q4_K | 1.11GB |
| [GPT2XL_RLLMv15-1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q4_K_M.gguf) | Q4_K_M | 1.11GB |
| [GPT2XL_RLLMv15-1.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q4_1.gguf) | Q4_1 | 1.0GB |
| [GPT2XL_RLLMv15-1.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q5_0.gguf) | Q5_0 | 1.09GB |
| [GPT2XL_RLLMv15-1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q5_K_S.gguf) | Q5_K_S | 1.15GB |
| [GPT2XL_RLLMv15-1.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q5_K.gguf) | Q5_K | 1.29GB |
| [GPT2XL_RLLMv15-1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q5_K_M.gguf) | Q5_K_M | 1.29GB |
| [GPT2XL_RLLMv15-1.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q5_1.gguf) | Q5_1 | 1.18GB |
| [GPT2XL_RLLMv15-1.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q6_K.gguf) | Q6_K | 1.52GB |
| [GPT2XL_RLLMv15-1.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv15-1-gguf/blob/main/GPT2XL_RLLMv15-1.Q8_0.gguf) | Q8_0 | 1.63GB |
Original model description:
---
license: mit
---
|
Tristan/dclm-random-1b-openbookqa-gs5 | Tristan | "2025-04-04T21:50:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:48:32Z" | ---
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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Agoboy/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_elusive_kingfisher | Agoboy | "2025-04-04T21:47:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tawny elusive kingfisher",
"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 | "2025-04-04T00:01:57Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_elusive_kingfisher
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tawny elusive kingfisher
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_elusive_kingfisher
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="Agoboy/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tawny_elusive_kingfisher", 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 GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tristan/dclm-perplexity-correlations-160m-3-openbookqa-gs9 | Tristan | "2025-04-04T21:46:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:46:17Z" | ---
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. -->
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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
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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).
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Tristan/dclm-perplexity-correlations-1b-3-openbookqa-gs4 | Tristan | "2025-04-04T21:45:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:43:37Z" | ---
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 π€ 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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<!-- 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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[More Information Needed]
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hammmile/SD_Yoshitomo_Nara_LORA | hammmile | "2025-04-04T21:45:13Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-04-04T21:41:58Z" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: photo in my style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- 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. -->
# SDXL LoRA DreamBooth - hammmile/SD_Yoshitomo_Nara_LORA
<Gallery />
## Model description
These are hammmile/SD_Yoshitomo_Nara_LORA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use photo in my style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](hammmile/SD_Yoshitomo_Nara_LORA/tree/main) them in the Files & versions tab.
## 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-id-1b-openbookqa-gs10 | Tristan | "2025-04-04T21:43:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:41:36Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[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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[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]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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melnnnnn/just-done-v5 | melnnnnn | "2025-04-04T21:43:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T21:43:14Z" | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** melnnnnn
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 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)
|
genki10/BERT_AugV8_k7_task1_organization_sp030_lw050_fold4 | genki10 | "2025-04-04T21:41:52Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T21:28:09Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k7_task1_organization_sp030_lw050_fold4
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. -->
# BERT_AugV8_k7_task1_organization_sp030_lw050_fold4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8458
- Qwk: 0.3715
- Mse: 0.8458
- Rmse: 0.9197
## 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: 64
- eval_batch_size: 64
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 6 | 6.9986 | 0.0018 | 6.9986 | 2.6455 |
| No log | 2.0 | 12 | 3.2986 | 0.0040 | 3.2986 | 1.8162 |
| No log | 3.0 | 18 | 1.5889 | 0.0509 | 1.5889 | 1.2605 |
| No log | 4.0 | 24 | 2.5077 | 0.0633 | 2.5077 | 1.5836 |
| No log | 5.0 | 30 | 1.6784 | 0.1400 | 1.6784 | 1.2955 |
| No log | 6.0 | 36 | 1.2747 | 0.0694 | 1.2747 | 1.1290 |
| No log | 7.0 | 42 | 0.8171 | 0.3794 | 0.8171 | 0.9040 |
| No log | 8.0 | 48 | 0.9671 | 0.3560 | 0.9671 | 0.9834 |
| No log | 9.0 | 54 | 0.6165 | 0.4170 | 0.6165 | 0.7852 |
| No log | 10.0 | 60 | 0.7601 | 0.4553 | 0.7601 | 0.8718 |
| No log | 11.0 | 66 | 1.0216 | 0.3715 | 1.0216 | 1.0107 |
| No log | 12.0 | 72 | 1.8056 | 0.1883 | 1.8056 | 1.3437 |
| No log | 13.0 | 78 | 0.8427 | 0.4465 | 0.8427 | 0.9180 |
| No log | 14.0 | 84 | 0.8376 | 0.4574 | 0.8376 | 0.9152 |
| No log | 15.0 | 90 | 1.0525 | 0.3200 | 1.0526 | 1.0259 |
| No log | 16.0 | 96 | 1.1972 | 0.3434 | 1.1972 | 1.0942 |
| No log | 17.0 | 102 | 0.9160 | 0.3961 | 0.9160 | 0.9571 |
| No log | 18.0 | 108 | 0.8218 | 0.4038 | 0.8218 | 0.9066 |
| No log | 19.0 | 114 | 0.9627 | 0.3569 | 0.9627 | 0.9812 |
| No log | 20.0 | 120 | 1.0769 | 0.3519 | 1.0769 | 1.0377 |
| No log | 21.0 | 126 | 1.1391 | 0.3710 | 1.1391 | 1.0673 |
| No log | 22.0 | 132 | 0.8961 | 0.3750 | 0.8961 | 0.9466 |
| No log | 23.0 | 138 | 0.8416 | 0.3645 | 0.8416 | 0.9174 |
| No log | 24.0 | 144 | 1.3868 | 0.2469 | 1.3868 | 1.1776 |
| No log | 25.0 | 150 | 1.4491 | 0.2486 | 1.4491 | 1.2038 |
| No log | 26.0 | 156 | 0.8228 | 0.3952 | 0.8228 | 0.9071 |
| No log | 27.0 | 162 | 0.8252 | 0.4050 | 0.8252 | 0.9084 |
| No log | 28.0 | 168 | 0.9975 | 0.3413 | 0.9975 | 0.9987 |
| No log | 29.0 | 174 | 0.8458 | 0.3715 | 0.8458 | 0.9197 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf | RichardErkhov | "2025-04-04T21:41:08Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T21:02:39Z" | 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-instruct-qlora-int4-eo8 - GGUF
- Model creator: https://huggingface.co/zackli4ai/
- Original model: https://huggingface.co/zackli4ai/llama-3.2-3b-instruct-qlora-int4-eo8/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q2_K.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_S.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_M.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q4_0.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q4_1.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q5_0.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q5_1.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q6_K.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama-3.2-3b-instruct-qlora-int4-eo8.Q8_0.gguf](https://huggingface.co/RichardErkhov/zackli4ai_-_llama-3.2-3b-instruct-qlora-int4-eo8-gguf/blob/main/llama-3.2-3b-instruct-qlora-int4-eo8.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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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[More Information Needed]
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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/multilingual-id-410m-raw-openbookqa-gs4 | Tristan | "2025-04-04T21:40:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:39:50Z" | ---
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 π€ 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. -->
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### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
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#### 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] |
MinaMila/phi3_unlearned_Adult_10ep_33 | MinaMila | "2025-04-04T21:39:02Z" | 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 | "2025-04-04T21:36:24Z" | ---
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)
|
Tristan/dclm-id-410m-openbookqa-gs0 | Tristan | "2025-04-04T21:38:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:38:11Z" | ---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Tristan/dclm-perplexity-correlations-410m-3-openbookqa-gs7 | Tristan | "2025-04-04T21:37:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:36:33Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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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### 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.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## 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).
- **Hardware Type:** [More Information Needed]
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BoranIsmet/0.1 | BoranIsmet | "2025-04-04T21:36:30Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-04T21:29:44Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: '0.1'
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. -->
# 0.1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7142
- Accuracy: 0.6462
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 0.6721 | 0.6029 |
| No log | 2.0 | 40 | 0.6689 | 0.5921 |
| No log | 3.0 | 60 | 0.6553 | 0.6173 |
| No log | 4.0 | 80 | 0.6527 | 0.6282 |
| No log | 5.0 | 100 | 0.6651 | 0.6282 |
| No log | 6.0 | 120 | 0.6669 | 0.6245 |
| No log | 7.0 | 140 | 0.7001 | 0.6209 |
| No log | 8.0 | 160 | 0.7142 | 0.6462 |
| No log | 9.0 | 180 | 0.7329 | 0.6137 |
| No log | 10.0 | 200 | 0.7414 | 0.6137 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Tristan/dclm-sampled-labels-410m-openbookqa-gs10 | Tristan | "2025-04-04T21:33:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-04T21:32:49Z" | ---
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 π€ 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]
### 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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### 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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[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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