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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 12:28:49
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
hdong0/deepseek-Qwen2.5-Math-7B-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc
|
hdong0
| 2025-06-19T22:28:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T18:33:46Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: deepseek-Qwen2.5-Math-7B-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for deepseek-Qwen2.5-Math-7B-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) 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="hdong0/deepseek-Qwen2.5-Math-7B-Open-R1-GRPO_deepscaler_1000steps_lr1e-6_kl1e-3_acc", 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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
stewy33/0524_true_augmented_original_pkc_ski_olympics-4d9dd867
|
stewy33
| 2025-06-19T22:17:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-06-19T22:14:51Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
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
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]
### Framework versions
- PEFT 0.15.1
|
aleegis/67f13971-098d-431f-8209-3df3dbf5681c
|
aleegis
| 2025-06-19T21:39:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:NousResearch/Nous-Capybara-7B-V1",
"base_model:finetune:NousResearch/Nous-Capybara-7B-V1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T21:35:53Z |
---
base_model: NousResearch/Nous-Capybara-7B-V1
library_name: transformers
model_name: 67f13971-098d-431f-8209-3df3dbf5681c
tags:
- generated_from_trainer
- axolotl
- trl
- grpo
licence: license
---
# Model Card for 67f13971-098d-431f-8209-3df3dbf5681c
This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1).
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="aleegis/67f13971-098d-431f-8209-3df3dbf5681c", 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/fajarchen-fajar-chen/Gradients-On-Demand/runs/c57i097i)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.1
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
IncreasingLoss/FineTunedTranslation_Yi-1.5-9B-Chat-Q6_K
|
IncreasingLoss
| 2025-06-19T21:24:55Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-19T19:45:35Z |
---
license: apache-2.0
---
|
rodrigomt/gama-4b-Q4_K_M-GGUF
|
rodrigomt
| 2025-06-19T20:57:58Z | 0 | 0 | null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it",
"soob3123/Veiled-Calla-4B",
"soob3123/amoral-gemma3-4B-v2-qat",
"llama-cpp",
"gguf-my-repo",
"base_model:rodrigomt/gama-4b",
"base_model:quantized:rodrigomt/gama-4b",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-19T20:57:44Z |
---
base_model: rodrigomt/gama-4b
tags:
- merge
- mergekit
- lazymergekit
- CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it
- soob3123/Veiled-Calla-4B
- soob3123/amoral-gemma3-4B-v2-qat
- llama-cpp
- gguf-my-repo
---
# rodrigomt/gama-4b-Q4_K_M-GGUF
This model was converted to GGUF format from [`rodrigomt/gama-4b`](https://huggingface.co/rodrigomt/gama-4b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/rodrigomt/gama-4b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo rodrigomt/gama-4b-Q4_K_M-GGUF --hf-file gama-4b-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo rodrigomt/gama-4b-Q4_K_M-GGUF --hf-file gama-4b-q4_k_m-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo rodrigomt/gama-4b-Q4_K_M-GGUF --hf-file gama-4b-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo rodrigomt/gama-4b-Q4_K_M-GGUF --hf-file gama-4b-q4_k_m-imat.gguf -c 2048
```
|
johngreendr1/a534258f-0c05-431a-a582-4504cbdbd9c9
|
johngreendr1
| 2025-06-19T20:50:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:upstage/SOLAR-10.7B-Instruct-v1.0",
"base_model:adapter:upstage/SOLAR-10.7B-Instruct-v1.0",
"region:us"
] | null | 2025-06-19T15:32:32Z |
---
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
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
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]
### Framework versions
- PEFT 0.15.1
|
VIDEOS-18-mezzo-fun-19-Viral-videos/New.tutorial.mezzo.fun.Viral.Video.Leaks.Official
|
VIDEOS-18-mezzo-fun-19-Viral-videos
| 2025-06-19T19:55:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T19:55:38Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
rajtiwariee/gemma-3-finetune
|
rajtiwariee
| 2025-06-19T19:31:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T19:29:55Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** rajtiwariee
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
Fredaaaaaa/hybrid_model
|
Fredaaaaaa
| 2025-06-19T19:17:28Z | 17 | 0 | null |
[
"pytorch",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-04-23T15:46:38Z |
---
license: apache-2.0
---
|
naomiKenKorem/LTXV_13B_LoRA_smpl
|
naomiKenKorem
| 2025-06-19T19:15:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"ltx-video",
"image-to-video",
"text-to-video",
"en",
"license:other",
"region:us"
] |
text-to-video
| 2025-06-19T19:14:42Z |
---
tags:
- ltx-video
- image-to-video
pinned: true
language:
- en
license: other
pipeline_tag: text-to-video
library_name: diffusers
---
# LTXV_13B_LoRA_smpl
This is a fine-tuned version of [`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors) trained on custom data.
## Model Details
- **Base Model:** [`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors)
- **Training Type:** LoRA fine-tuning
- **Training Steps:** 4000
- **Learning Rate:** 0.0002
- **Batch Size:** 1
## Sample Outputs
| | | | |
|:---:|:---:|:---:|:---:|
| <br><details style="max-width: 300px; margin: auto;"><summary>Prompt</summary>people dancing</details> | <br><details style="max-width: 300px; margin: auto;"><summary>Prompt</summary>a woman talking</details> | <br><details style="max-width: 300px; margin: auto;"><summary>Prompt</summary>a man skiing</details> |
## Usage
This model is designed to be used with the LTXV (Lightricks Text-to-Video) pipeline.
### 🔌 Using Trained LoRAs in ComfyUI
In order to use the trained lora in comfy:
1. Copy your comfyui trained LoRA weights (`comfyui..safetensors` file) to the `models/loras` folder in your ComfyUI installation.
2. In your ComfyUI workflow:
- Add the "LTXV LoRA Selector" node to choose your LoRA file
- Connect it to the "LTXV LoRA Loader" node to apply the LoRA to your generation
You can find reference Text-to-Video (T2V) and Image-to-Video (I2V) workflows in the [official LTXV ComfyUI repository](https://github.com/Lightricks/ComfyUI-LTXVideo).
### Example Prompts
Example prompts used during validation:
- `people dancing`
- `a woman talking`
- `a man skiing`
This model inherits the license of the base model ([`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors)).
## Acknowledgments
- Base model by [Lightricks](https://huggingface.co/Lightricks)
- Training infrastructure: [LTX-Video-Trainer](https://github.com/Lightricks/ltx-video-trainer)
|
ISAAC-XYN1-MATT-KERVI-JAVIER-ISAAC-xx/ORIGINAL.VIDEO.18.ISAAC.XYN1.MATT.KERVI.JAVIER.ISAAC.X.VIRAL.ON.TWITTER
|
ISAAC-XYN1-MATT-KERVI-JAVIER-ISAAC-xx
| 2025-06-19T18:17:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T18:16:59Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
clip-videos-matt-kervi-javier-isaac-video/Matt.Kervi.Javier.Isaac.xyn.Viral.Video.Link.Official
|
clip-videos-matt-kervi-javier-isaac-video
| 2025-06-19T17:58:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T17:44:52Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
rodrigomt/veiled-japanse-Q8_0-GGUF
|
rodrigomt
| 2025-06-19T17:53:04Z | 0 | 0 | null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Aratako/gemma-3-4b-it-RP-v0.1",
"soob3123/Veiled-Calla-4B",
"llama-cpp",
"gguf-my-repo",
"base_model:rodrigomt/veiled-japanse",
"base_model:quantized:rodrigomt/veiled-japanse",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T17:52:42Z |
---
base_model: rodrigomt/veiled-japanse
tags:
- merge
- mergekit
- lazymergekit
- Aratako/gemma-3-4b-it-RP-v0.1
- soob3123/Veiled-Calla-4B
- llama-cpp
- gguf-my-repo
---
# rodrigomt/veiled-japanse-Q8_0-GGUF
This model was converted to GGUF format from [`rodrigomt/veiled-japanse`](https://huggingface.co/rodrigomt/veiled-japanse) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/rodrigomt/veiled-japanse) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo rodrigomt/veiled-japanse-Q8_0-GGUF --hf-file veiled-japanse-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo rodrigomt/veiled-japanse-Q8_0-GGUF --hf-file veiled-japanse-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo rodrigomt/veiled-japanse-Q8_0-GGUF --hf-file veiled-japanse-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo rodrigomt/veiled-japanse-Q8_0-GGUF --hf-file veiled-japanse-q8_0.gguf -c 2048
```
|
nanotechnologie/G-Medicale-R1
|
nanotechnologie
| 2025-06-19T17:47:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-19T17:24:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/KwaiCoder-AutoThink-preview-GGUF
|
mradermacher
| 2025-06-19T17:37:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"multilingual",
"base_model:Kwaipilot/KwaiCoder-AutoThink-preview",
"base_model:quantized:Kwaipilot/KwaiCoder-AutoThink-preview",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-19T10:35:08Z |
---
base_model: Kwaipilot/KwaiCoder-AutoThink-preview
language:
- multilingual
library_name: transformers
license: other
license_link: LICENSE
license_name: kwaipilot-license
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Kwaipilot/KwaiCoder-AutoThink-preview
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q2_K.gguf) | Q2_K | 15.3 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q3_K_S.gguf) | Q3_K_S | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q3_K_M.gguf) | Q3_K_M | 19.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q3_K_L.gguf) | Q3_K_L | 21.4 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.IQ4_XS.gguf) | IQ4_XS | 22.2 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q4_K_S.gguf) | Q4_K_S | 23.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q4_K_M.gguf) | Q4_K_M | 24.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q5_K_S.gguf) | Q5_K_S | 28.1 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q5_K_M.gguf) | Q5_K_M | 28.9 | |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q6_K.gguf) | Q6_K | 33.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KwaiCoder-AutoThink-preview-GGUF/resolve/main/KwaiCoder-AutoThink-preview.Q8_0.gguf) | Q8_0 | 43.2 | 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 -->
|
arnomatic/anna-gemma3-27b-uncensored
|
arnomatic
| 2025-06-19T17:32:46Z | 0 | 0 | null |
[
"gguf",
"roleplay",
"uncensored",
"chat",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-19T16:32:21Z |
---
license: gemma
tags:
- roleplay
- uncensored
- chat
---
# Anna - Uncensored Gemma-3 27B Chat
This is an uncensored fine-tune of Gemma-3 27B for conversation.
⚠️ **Warning**: This model is uncensored and may generate adult content.
## Training
- Base: google/gemma-3-27b-it
- Method: QLoRA fine-tuning
- Data: german/english conversation data
|
morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-2-seed-28-2025-06-19
|
morturr
| 2025-06-19T17:32:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T17:31:55Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-2-seed-28-2025-06-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-2-seed-28-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
gregkseno/csbm
|
gregkseno
| 2025-06-19T17:30:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text2text",
"image2image",
"domain_translation",
"optimal_transport",
"discrete_diffusion",
"schrödinger_bridge",
"text2text-generation",
"en",
"arxiv:2502.01416",
"arxiv:2405.14449",
"arxiv:2312.14708",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-16T21:45:34Z |
---
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
license: mit
metrics:
- fid
- cmmd
- lpips
- accuracy
- bleu
- nll
tags:
- text2text
- image2image
- domain_translation
- optimal_transport
- discrete_diffusion
- schrödinger_bridge
---
<div align="center">
# Categorical Schrödinger Bridge Matching (CSBM)
[Grigoriy Ksenofontov](https://scholar.google.com/citations?user=e0mirzYAAAAJ),
[Alexander Korotin](https://scholar.google.ru/citations?user=1rIIvjAAAAAJ)
[](https://arxiv.org/abs/2502.01416)
[](https://openreview.net/forum?id=RBly0nOr2h)
[](https://github.com/gregkseno/csbm)
[](https://huggingface.co/gregkseno/csbm)
[](https://wandb.ai/gregkseno/csbm)
</div>
This repository hosts the official checkpoints for the paper "Categorical Schrödinger Bridge Matching", accepted at ICML 2025.
## 📌 TL;DR
This paper extends the Schrödinger Bridge problem to work with discrete time and spaces.
<!--  -->
## 💾 Checkpoints
### CSBM
| Dataset | Reference Process | α | N | Saved Iteration |
| ------------- | ----------------- | ----------- | --------------------- | --------------- |
| Colored MNIST | **gaussian** | 0.01 | 2, 4, 10, 25, 50, 100 | 3 |
| Colored MNIST | **uniform** | 0.01, 0.05 | 25 | 3 |
| CelebA | **uniform** | 0.01, 0.005 | 100 | 4 |
| Amazon Review | **uniform** | 0.01, 0.005 | 100 | 5 |
> [!NOTE]
> Each experiment directory includes a `config.yaml` file with the full training configuration.
### Additional Components
1. `vqgan_celeba_f8_1024.ckpt` — **VQ-GAN** pretrained on the CelebA dataset
2. `tokenizer_amazon.json` — **Tokenizer** trained on the Amazon Reviews dataset
## 🎓 Citation
```bibtex
@inproceedings{
ksenofontov2025categorical,
title={Categorical {Schr\"odinger} Bridge Matching},
author={Grigoriy Ksenofontov and Alexander Korotin},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=RBly0nOr2h}
}
```
## 🙏 Credits
- [Weights & Biases](https://wandb.ai) — experiment-tracking and visualization toolkit;
- [Hugging Face](https://huggingface.co) — Tokenizers and Accelerate libraries for tokenizer implementation, parallel training, and checkpoint hosting on the Hub;
- [D3PM](https://github.com/google-research/google-research/tree/master/d3pm) — reference implementation of discrete-diffusion models;
- [Taming Transformers](https://github.com/CompVis/taming-transformers) — original VQ-GAN codebase;
- [VQ-Diffusion](https://github.com/microsoft/VQ-Diffusion) — vector-quantized diffusion architecture;
- [MDLM](https://github.com/kuleshov-group/mdlm) — diffusion architecture for text-generation experiments;
- [ASBM](https://arxiv.org/abs/2405.14449) — evaluation metrics and baseline models for CelebA face transfer;
- [Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising](https://arxiv.org/abs/2312.14708) — processed Amazon Reviews dataset and sentiment-transfer baselines;
- [Inkscape](https://inkscape.org/) — an excellent open-source editor for vector graphics.
|
diffusion-reasoning/sudoku-wll-neg-pos-2500
|
diffusion-reasoning
| 2025-06-19T16:53:28Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:GSAI-ML/LLaDA-8B-Instruct",
"base_model:adapter:GSAI-ML/LLaDA-8B-Instruct",
"region:us"
] | null | 2025-06-19T16:53:16Z |
---
base_model: GSAI-ML/LLaDA-8B-Instruct
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
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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
Official-Tv-mezzo-fun-18-19-Viral-videos/FULL.VIDEO.LINK.mezzo.fun.Viral.Video.Leaks.Tutorial.Official
|
Official-Tv-mezzo-fun-18-19-Viral-videos
| 2025-06-19T16:36:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T16:34:30Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ljnlonoljpiljm/florence-2-base-ft-humancaptions-mlx
|
ljnlonoljpiljm
| 2025-06-19T16:23:54Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"florence2",
"text-generation",
"mlx",
"custom_code",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-06-17T21:55:48Z |
---
library_name: transformers
tags:
- mlx
---
# ljnlonoljpiljm/florence-2-base-ft-humancaptions-mlx
This model was converted to MLX format from [`ljnlonoljpiljm/florence-2-base-ft-humancaptions`]() using mlx-vlm version **0.1.13**.
Refer to the [original model card](https://huggingface.co/ljnlonoljpiljm/florence-2-base-ft-humancaptions) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model ljnlonoljpiljm/florence-2-base-ft-humancaptions-mlx --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
kenooo/multisense-tinyllama-finetune
|
kenooo
| 2025-06-19T16:19:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T16:17:02Z |
---
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 🤗 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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## Uses
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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
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#### 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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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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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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lamdo/distilbert-base-uncased-aol-concepts
|
lamdo
| 2025-06-19T16:04:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-19T16:04:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
alfredcs/gemma-3-27b-firstaid-icd10-merged
|
alfredcs
| 2025-06-19T15:23:10Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gemma3",
"image-text-to-text",
"trl",
"grpo",
"GRPO",
"Reasoning-Course",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T05:13:36Z |
---
library_name: transformers
tags:
- trl
- grpo
- GRPO
- Reasoning-Course
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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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]
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[More Information Needed]
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[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. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
joedonino/gemma-product-description-v6-merged
|
joedonino
| 2025-06-19T15:15:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T15:10:23Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[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. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v8
|
Salmaalaa
| 2025-06-19T14:49:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:finetune:codellama/CodeLlama-7b-Instruct-hf",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:03:30Z |
---
base_model: codellama/CodeLlama-7b-Instruct-hf
library_name: transformers
model_name: CodeLlama-7b-Instruct_AR2SQL_v8
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for CodeLlama-7b-Instruct_AR2SQL_v8
This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf).
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="Salmaalaa/CodeLlama-7b-Instruct_AR2SQL_v8", 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 SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
alakxender/flan-t5-base-alpaca-dv
|
alakxender
| 2025-06-19T14:47:18Z | 28 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"dhivehi",
"gpt",
"llm",
"thaana",
"text-gen",
"dv",
"dataset:alakxender/alpaca_dhivehi",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-30T12:15:21Z |
---
library_name: transformers
tags:
- dhivehi
- gpt
- llm
- thaana
- text-gen
license: mit
datasets:
- alakxender/alpaca_dhivehi
language:
- dv
metrics:
- rouge
base_model:
- google/flan-t5-base
---
# Alpaca Dhivehi Fine-Tuned Flan-T5
This repository contains a **fine-tuned Flan-T5** model on the **Alpaca Dhivehi dataset**, aimed at enabling Dhivehi language instruction-following tasks.
***Note: The model can follow instructions and inputs to some extent, but it’s not strictly trained for perfect adherence. Outputs may be partially aligned but are not guaranteed to be fully accurate. Treat results as experimental.***
## Model Details
- **Base model**: `google/flan-t5-small` (or whichever size you used)
- **Dataset**: Alpaca Dhivehi , Translation from English to Dhivehi
- **Training epochs**: 3
- **Final evaluation**:
- `eval_loss`: 2.94
- `ROUGE-1`: 0.10
- `ROUGE-2`: 0.03
- `ROUGE-L`: 0.099
- Runtime: 1191 seconds (~20 minutes)
- ~4.3 samples/sec, ~2.17 steps/sec
## Usage
To **run inference** using the fine-tuned model:
```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
MODEL_PATH = "alakxender/flan-t5-base-alpaca-dv"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = T5Tokenizer.from_pretrained(MODEL_PATH)
model = T5ForConditionalGeneration.from_pretrained(MODEL_PATH).to(device)
def generate_response(instruction, input_text):
combined_input = f"{instruction.strip()} {input_text.strip()}" if input_text else instruction.strip()
inputs = tokenizer(combined_input, return_tensors="pt", truncation=True, max_length=256).to(device)
output_ids = model.generate(
**inputs,
max_new_tokens=256,
num_beams=8,
repetition_penalty=1.5,
no_repeat_ngram_size=3,
do_sample=True,
early_stopping=True,
temperature=0.1
)
decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return decoded_output
# Example usage:
instruction = "ދީފައިވާ މައުޟޫޢާ ބެހޭގޮތުން ކުރު ޕެރެގްރާފެއް ލިޔެލާށެވެ."
input_text = "އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމުކަން"
print(generate_response(instruction, input_text))
އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމު އެއް މައުޟޫއަކީ ސޯލާ، ވިންޑް، ހައިޑްރޯ، ޖިއޮތަރމަލް، އަދި ހައިޑްރޯއިލެކްޓްރިކް ޕަވަރ ފަދަ އިއާދަކުރަނިވި ހަކަތައިން ގްރީންހައުސް ގޭސްތައް ބޭރުވުން .....
```
## Evaluation Results
From the last evaluation:
```
{'eval_loss': 2.9449,
'eval_rouge1': 0.1003,
'eval_rouge2': 0.0335,
'eval_rougeL': 0.0993,
'eval_rougeLsum': 0.0993,
'eval_runtime': 1191.5,
'eval_samples_per_second': 4.34,
'eval_steps_per_second': 2.17,
'epoch': 3.0}
```
## Notes
- This fine-tuned model is experimental and intended for research on Dhivehi-language instruction-following tasks.
|
TruongSinhAI/Qwen2.5-1.5B-Instruct_200steps
|
TruongSinhAI
| 2025-06-19T14:12:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T14:12:00Z |
---
base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TruongSinhAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-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)
|
wolfCuanhamaRWS/Llama-Primus-Reasoning_q2_k_gguf
|
wolfCuanhamaRWS
| 2025-06-19T13:57:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"thesis_quant",
"q2_k_gguf",
"text-classification",
"en",
"arxiv:2501.18492",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-classification
| 2025-06-19T13:54:48Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B
tags:
- llama-factory
- full
- generated_from_trainer
- thesis_quant
- q2_k_gguf
pipeline_tag: text-classification
language:
- en
metrics:
- f1
model-index:
- name: GuardReasoner 1B
results: []
---
# GuardReasoner 1B
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492).
The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain).
Code: https://github.com/yueliu1999/GuardReasoner/
# Usage
```
import re
from vllm import LLM, SamplingParams
INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n"
def post_process(text):
text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE)
return text
def generate(vllm_model, prompt_list=[""], response_list=["None"]):
input_list = []
for i in range(len(prompt_list)):
input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n"
input_list.append(input)
outputs = vllm_model.generate(input_list, sampling_params)
return outputs
vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256)
sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048)
prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."]
response_list = ["""Dear LinkedIn friends,
Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely.
The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day.
It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly.
I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection.
Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change.
Sincerely,
Mark
"""]
output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text)
print(output)
```
# Citation
```
@article{GuardReasoner,
title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan},
journal={arXiv preprint arXiv:2501.18492},
year={2025}
}
```
|
wolfCuanhamaRWS/WhiteRabbitNeo-V3-7B_q5_k_m_gguf
|
wolfCuanhamaRWS
| 2025-06-19T13:29:24Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"thesis_quant",
"q5_k_m_gguf",
"text-classification",
"en",
"arxiv:2501.18492",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-classification
| 2025-06-19T13:25:36Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B
tags:
- llama-factory
- full
- generated_from_trainer
- thesis_quant
- q5_k_m_gguf
pipeline_tag: text-classification
language:
- en
metrics:
- f1
model-index:
- name: GuardReasoner 1B
results: []
---
# GuardReasoner 1B
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492).
The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain).
Code: https://github.com/yueliu1999/GuardReasoner/
# Usage
```
import re
from vllm import LLM, SamplingParams
INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n"
def post_process(text):
text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE)
return text
def generate(vllm_model, prompt_list=[""], response_list=["None"]):
input_list = []
for i in range(len(prompt_list)):
input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n"
input_list.append(input)
outputs = vllm_model.generate(input_list, sampling_params)
return outputs
vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256)
sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048)
prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."]
response_list = ["""Dear LinkedIn friends,
Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely.
The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day.
It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly.
I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection.
Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change.
Sincerely,
Mark
"""]
output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text)
print(output)
```
# Citation
```
@article{GuardReasoner,
title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan},
journal={arXiv preprint arXiv:2501.18492},
year={2025}
}
```
|
mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF
|
mradermacher
| 2025-06-19T13:07:19Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:buttercoconut/Qwen2.5-ko-alpaca-0.5B",
"base_model:quantized:buttercoconut/Qwen2.5-ko-alpaca-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T13:02:49Z |
---
base_model: buttercoconut/Qwen2.5-ko-alpaca-0.5B
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/buttercoconut/Qwen2.5-ko-alpaca-0.5B
<!-- 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/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19
|
morturr
| 2025-06-19T13:00:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T13:00:08Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
mchettih/financial_QA_unsloth_Llama-3.2-3B-Instruct_finetuned_teacher
|
mchettih
| 2025-06-19T12:59:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T15:59:34Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mchettih
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
abhinavpuranik/layoutlmv3-financial-document-classification
|
abhinavpuranik
| 2025-06-19T12:26:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-19T12:25:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
smakov/rut5_medsum
|
smakov
| 2025-06-19T12:07:40Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"medical",
"summarization",
"ru",
"dataset:smakov/ru_medsum",
"arxiv:1109.2128",
"base_model:ai-forever/ruT5-base",
"base_model:finetune:ai-forever/ruT5-base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2025-06-17T10:48:06Z |
---
library_name: transformers
tags:
- medical
language:
- ru
base_model:
- ai-forever/ruT5-base
pipeline_tag: summarization
datasets:
- smakov/ru_medsum
metrics:
- bleu
- rouge
- meteor
---
# ruT5_MedSum
## Model description
This is a finetuned version of [ai-forevert/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on summarization task of medical abstracts.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "smakov/rut5_medsum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
article_text = "введение. новый коронавирус тяжелого острого респираторного синдрома 2 (sars-cov-2) был объявлен всемирной организацией здравоохраненияпандемиейв марте 2020 г. новые волны различных генотипов вируса регулярно прокатываются по земному шару. последние исследования демонстрируют худшие прогнозы для пациентов с имеющимся сердечнососудистым заболеванием, указывая на возможную связь между инфекциейsars-cov-2и увеличением числа новых случаевсердечно-сосудистых заболеванийи диабета независимо от тяжести патологии. если эта тенденция верна, то с сотнями миллионов инфицированных бремя болезней может предвещать потенциально тревожный ростсердечно-сосудистых заболеванийи диабета в будущем.цель - изучить лабораторные показатели пациентов с новой коронавирусной инфекцией covid-19 на фоне сахарного диабета (сд) 2-го типа и хронической сердечной недостаточности.материалы и методы. в исследование включен 51 пациент с верифицированных диагнозом новой короновирусной инфекции covid-19 средней степени тяжести на фоне сд 2-го типа и хронической сердечной недостаточностью. всем пациентам проведено количественное определение лабораторных показателей крови. корреляционный анализ проводился с использованием коэффициента ранговойкорреляции спирмена.результаты и обсуждение. наиболее интересными, на наш взгляд, оказались многочисленные корреляционные связи между концентрацией глюкозы и маркерами дисфункции внутренних органов и систем: с концентрацией билирубина, международным нормализованным отношением, креатинином, с концентрацией углекислого газа (ctco2 ), оснований (ве), с концентрацией бикарбоната (hco3 act). наиболее выраженной (rs = 0,74) оказалась корреляционная связь концентрации глюкозы с количеством базофилов, что может свидетельствовать о возможном влиянии механизмов гиперчувствительности на выраженность гипергликемии у пациентов с covid-19.выводы. уровень гипергликемии - это маркер выраженности не только метаболического ацидоза, но и дисфункции внутренних органов и систем у пациентов с новой коронавирусной инфекцией covid-19, протекающей на фоне сд 2-го типа и сердечной недостаточности."
input_ids = tokenizer(
[article_text],
max_length=512,
padding="max_length",
truncation=True,
return_tensors="pt",
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=max_length,
num_beams=3,
early_stopping=True
)[0]
summary = tokenizer.decode(output_ids, skip_special_tokens=True)
print(summary)
# 'лабораторные показатели пациентов с новой коронавирусной инфекцией covid-19 на фоне сахарного диабета 2-го типа и хронической сердечной недостаточности'
```
## Training data
- Dataset: [ru_MedSum](https://huggingface.co/datasets/smakov/ru_medsum)
## Training procedure
- Training notebook: [train.ipynb](https://github.com/smvkvv/rut5_medsum/blob/main/train.ipynb)
## Evaluating
- Evaluating notebook: [evaluate.ipynb](https://github.com/smvkvv/rut5_medsum/blob/main/evaluate.ipynb)
## Eval results
* Train dataset: **ru_MedSum train**
* Test dataset: **ru_MedSum test**
* Source max_length: **512**
* Target max_length: **128**
* num_beams: **3**
| Model | R-1-f | R-2-f | R-L-f | BLEU | METEOR | Avg char length |
|:--------------------------|:------|:------|:------|:-------|:-------|:----------------|
| LSA | 23.1 | 14.3 | 18.5 | 2.6 | 14.7 | 220 |
| Luhn | 22.9 | 14.7 | 18.8 | 2.4 | 16.2 | 252 |
| TextRank | 20.1 | 12.1 | 15.8 | 1.7 | 14.7 | 304 |
| LexRank ([paper](https://arxiv.org/abs/1109.2128)) | 26.0 | 17.6 | 22.0 | 3.5 | 17.3 | 197 |
| [cointegrated/rut5-base-absum](https://huggingface.co/cointegrated/rut5-base-absum) | 29.9 | 21.1 | 26.1 | 6.2 | 17.7 | 127 |
| [smakov/rut5_medsum](https://huggingface.co/smakov/rut5_medsum) | 54.1 | 44.9 | 50.1 | 23.6 | 38.6 | 99 |
|
samil24/wav2vec2-large-xls-r-kurmanji_new_v5
|
samil24
| 2025-06-19T12:03:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-19T07:57:37Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-kurmanji_new_v5
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. -->
# wav2vec2-large-xls-r-kurmanji_new_v5
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7610
- Wer: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.7304 | 2.6596 | 500 | 3.4397 | 1.0 |
| 3.0857 | 5.3191 | 1000 | 3.0635 | 1.0 |
| 3.0271 | 7.9787 | 1500 | 3.0048 | 1.0 |
| 2.1462 | 10.6383 | 2000 | 1.7610 | 0.9990 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
|
winglian/tulu3-dpo-8b-qat-v1-fixed
|
winglian
| 2025-06-19T11:58:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"arxiv:2305.18290",
"base_model:allenai/Llama-3.1-Tulu-3-8B-SFT",
"base_model:finetune:allenai/Llama-3.1-Tulu-3-8B-SFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T11:55:06Z |
---
base_model: allenai/Llama-3.1-Tulu-3-8B-SFT
library_name: transformers
model_name: outputs/tulu3-dpo
tags:
- generated_from_trainer
licence: license
---
# Model Card for outputs/tulu3-dpo
This model is a fine-tuned version of [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT).
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="None", 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/axolotl-ai/qat-dpo-tulu3-8b/runs/z92cdxvr)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Khruna/tyler
|
Khruna
| 2025-06-19T11:21:49Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T11:21:35Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: >-
images/Professional_Mode_woman_shows_her_shiny_plate.00_00_29_20.Still002.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# tyler
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Khruna/tyler/tree/main) them in the Files & versions tab.
|
New-tutorial-Othoi-18-Viral-Videos/FULL.VIDEO.Othoi.Viral.Video.Tutorial.Official
|
New-tutorial-Othoi-18-Viral-Videos
| 2025-06-19T11:00:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T11:00:34Z |
<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a>
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb3-seed28-2025-06-19
|
morturr
| 2025-06-19T10:46:22Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T10:46:15Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb3-seed28-2025-06-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb3-seed28-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
alana89/TabSTAR-eval-320-version-fold-k0
|
alana89
| 2025-06-19T10:43:37Z | 0 | 0 | null |
[
"safetensors",
"tabstar",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T10:41:37Z |
---
license: apache-2.0
---
|
DevQuasar/NousResearch.DeepHermes-3-Llama-3-8B-Preview-GGUF
|
DevQuasar
| 2025-06-19T09:42:51Z | 145 | 3 | null |
[
"gguf",
"text-generation",
"base_model:NousResearch/DeepHermes-3-Llama-3-8B-Preview",
"base_model:quantized:NousResearch/DeepHermes-3-Llama-3-8B-Preview",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-02-13T21:52:41Z |
---
base_model:
- NousResearch/DeepHermes-3-Llama-3-8B-Preview
pipeline_tag: text-generation
license: llama3
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [NousResearch/DeepHermes-3-Llama-3-8B-Preview](https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview)
License: please refer the base model's license!
According to the original description
### Deep Thinking Mode - Deep Hermes Preview can activate long chain of thought with a system prompt.
```
You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.
```
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
John6666/moonmilk-contrast-v10-sdxl
|
John6666
| 2025-06-19T09:27:04Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"anime style",
"high contrast",
"pastel colors",
"portrait focus",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-06-19T09:21:03Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- anime style
- high contrast
- pastel colors
- portrait focus
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1693666/moonmilk-contrast?modelVersionId=1916780).
This model created by [Neural_Lens](https://civitai.com/user/Neural_Lens).
|
videos-mezzo-fun-Viral-Video-Original-Link/FULL.VIDEO.mezzo.fun.viral.video.viral.On.Social.Media.Official
|
videos-mezzo-fun-Viral-Video-Original-Link
| 2025-06-19T09:02:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T09:01:44Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Official-mezzo-fun-18-Viral-videos-18d/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
Official-mezzo-fun-18-Viral-videos-18d
| 2025-06-19T08:55:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T08:54:32Z |
<a href="https://mswds.xyz/full-video/?v=Mezzo-fun" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a>
<a href="https://mswds.xyz/full-video/?v=Mezzo-fun" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a>
<a href="https://mswds.xyz/full-video/?v=Mezzo-fun"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a>
|
zeblok/zeblok
|
zeblok
| 2025-06-19T08:42:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T13:36:07Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
makataomu/q-Taxi-v3-v1
|
makataomu
| 2025-06-19T07:56:13Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T07:56:09Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="makataomu/q-Taxi-v3-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
RedbeardNZ/bigvgan_v2_44khz_128band_512x
|
RedbeardNZ
| 2025-06-19T07:14:17Z | 0 | 0 |
PyTorch
|
[
"PyTorch",
"neural-vocoder",
"audio-generation",
"audio-to-audio",
"arxiv:2206.04658",
"license:mit",
"region:us"
] |
audio-to-audio
| 2025-06-19T07:14:17Z |
---
license: mit
license_link: https://huggingface.co/nvidia/BigVGAN/blob/main/LICENSE
tags:
- neural-vocoder
- audio-generation
library_name: PyTorch
pipeline_tag: audio-to-audio
---
## BigVGAN: A Universal Neural Vocoder with Large-Scale Training
#### Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon
[[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN)
[](https://paperswithcode.com/sota/speech-synthesis-on-libritts?p=bigvgan-a-universal-neural-vocoder-with-large)
<center><img src="https://user-images.githubusercontent.com/15963413/218609148-881e39df-33af-4af9-ab95-1427c4ebf062.png" width="800"></center>
## News
- **Jul 2024 (v2.3):**
- General refactor and code improvements for improved readability.
- Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark.
- **Jul 2024 (v2.2):** The repository now includes an interactive local demo using gradio.
- **Jul 2024 (v2.1):** BigVGAN is now integrated with 🤗 Hugging Face Hub with easy access to inference using pretrained checkpoints. We also provide an interactive demo on Hugging Face Spaces.
- **Jul 2024 (v2):** We release BigVGAN-v2 along with pretrained checkpoints. Below are the highlights:
- Custom CUDA kernel for inference: we provide a fused upsampling + activation kernel written in CUDA for accelerated inference speed. Our test shows 1.5 - 3x faster speed on a single A100 GPU.
- Improved discriminator and loss: BigVGAN-v2 is trained using a multi-scale sub-band CQT discriminator and a multi-scale mel spectrogram loss.
- Larger training data: BigVGAN-v2 is trained using datasets containing diverse audio types, including speech in multiple languages, environmental sounds, and instruments.
- We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling ratio.
## Installation
This repository contains pretrained BigVGAN checkpoints with easy access to inference and additional `huggingface_hub` support.
If you are interested in training the model and additional functionalities, please visit the official GitHub repository for more information: https://github.com/NVIDIA/BigVGAN
```shell
git lfs install
git clone https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x
```
## Usage
Below example describes how you can use BigVGAN: load the pretrained BigVGAN generator from Hugging Face Hub, compute mel spectrogram from input waveform, and generate synthesized waveform using the mel spectrogram as the model's input.
```python
device = 'cuda'
import torch
import bigvgan
import librosa
from meldataset import get_mel_spectrogram
# instantiate the model. You can optionally set use_cuda_kernel=True for faster inference.
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=False)
# remove weight norm in the model and set to eval mode
model.remove_weight_norm()
model = model.eval().to(device)
# load wav file and compute mel spectrogram
wav_path = '/path/to/your/audio.wav'
wav, sr = librosa.load(wav_path, sr=model.h.sampling_rate, mono=True) # wav is np.ndarray with shape [T_time] and values in [-1, 1]
wav = torch.FloatTensor(wav).unsqueeze(0) # wav is FloatTensor with shape [B(1), T_time]
# compute mel spectrogram from the ground truth audio
mel = get_mel_spectrogram(wav, model.h).to(device) # mel is FloatTensor with shape [B(1), C_mel, T_frame]
# generate waveform from mel
with torch.inference_mode():
wav_gen = model(mel) # wav_gen is FloatTensor with shape [B(1), 1, T_time] and values in [-1, 1]
wav_gen_float = wav_gen.squeeze(0).cpu() # wav_gen is FloatTensor with shape [1, T_time]
# you can convert the generated waveform to 16 bit linear PCM
wav_gen_int16 = (wav_gen_float * 32767.0).numpy().astype('int16') # wav_gen is now np.ndarray with shape [1, T_time] and int16 dtype
```
## Using Custom CUDA Kernel for Synthesis
You can apply the fast CUDA inference kernel by using a parameter `use_cuda_kernel` when instantiating BigVGAN:
```python
import bigvgan
model = bigvgan.BigVGAN.from_pretrained('nvidia/bigvgan_v2_44khz_128band_512x', use_cuda_kernel=True)
```
When applied for the first time, it builds the kernel using `nvcc` and `ninja`. If the build succeeds, the kernel is saved to `alias_free_activation/cuda/build` and the model automatically loads the kernel. The codebase has been tested using CUDA `12.1`.
Please make sure that both are installed in your system and `nvcc` installed in your system matches the version your PyTorch build is using.
For detail, see the official GitHub repository: https://github.com/NVIDIA/BigVGAN?tab=readme-ov-file#using-custom-cuda-kernel-for-synthesis
## Pretrained Models
We provide the [pretrained models on Hugging Face Collections](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a).
One can download the checkpoints of the generator weight (named `bigvgan_generator.pt`) and its discriminator/optimizer states (named `bigvgan_discriminator_optimizer.pt`) within the listed model repositories.
| Model Name | Sampling Rate | Mel band | fmax | Upsampling Ratio | Params | Dataset | Steps | Fine-Tuned |
|:--------------------------------------------------------------------------------------------------------:|:-------------:|:--------:|:-----:|:----------------:|:------:|:--------------------------:|:-----:|:----------:|
| [bigvgan_v2_44khz_128band_512x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_512x) | 44 kHz | 128 | 22050 | 512 | 122M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_44khz_128band_256x](https://huggingface.co/nvidia/bigvgan_v2_44khz_128band_256x) | 44 kHz | 128 | 22050 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_24khz_100band_256x](https://huggingface.co/nvidia/bigvgan_v2_24khz_100band_256x) | 24 kHz | 100 | 12000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_256x) | 22 kHz | 80 | 11025 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_v2_22khz_80band_fmax8k_256x](https://huggingface.co/nvidia/bigvgan_v2_22khz_80band_fmax8k_256x) | 22 kHz | 80 | 8000 | 256 | 112M | Large-scale Compilation | 5M | No |
| [bigvgan_24khz_100band](https://huggingface.co/nvidia/bigvgan_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 112M | LibriTTS | 5M | No |
| [bigvgan_base_24khz_100band](https://huggingface.co/nvidia/bigvgan_base_24khz_100band) | 24 kHz | 100 | 12000 | 256 | 14M | LibriTTS | 5M | No |
| [bigvgan_22khz_80band](https://huggingface.co/nvidia/bigvgan_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 112M | LibriTTS + VCTK + LJSpeech | 5M | No |
| [bigvgan_base_22khz_80band](https://huggingface.co/nvidia/bigvgan_base_22khz_80band) | 22 kHz | 80 | 8000 | 256 | 14M | LibriTTS + VCTK + LJSpeech | 5M | No |
|
huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2
|
huihui-ai
| 2025-06-19T05:44:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"chat",
"abliterated",
"uncensored",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T04:26:17Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-1.7B
tags:
- chat
- abliterated
- uncensored
---
# huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2
This is an uncensored version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
Ablation was performed using a new and faster method, which yields better results.
**Important Note** This version is an improvement over the previous one [huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated). The ollama version has also been modified.
Changed 0 layer to eliminate the problem of garbled codes
## ollama
You can use [huihui_ai/qwen3-abliterated:1.7b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:1.7b-v2) directly, Switch the thinking toggle using /set think and /set nothink
```
ollama run huihui_ai/qwen3-abliterated:1.7b-v2
```
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
import random
import numpy as np
import time
from collections import Counter
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_4,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
nothink = False
same_seed = False
skip_prompt=True
skip_special_tokens=True
do_sample = True
def set_random_seed(seed=None):
"""Set random seed for reproducibility. If seed is None, use int(time.time())."""
if seed is None:
seed = int(time.time()) # Convert float to int
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # If using CUDA
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
return seed # Return seed for logging if needed
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
self.init_time = time.time() # Record initialization time
self.end_time = None # To store end time
self.first_token_time = None # To store first token generation time
self.token_count = 0 # To track total tokens
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
self.first_token_time = time.time()
self.generated_text += text
# Count tokens in the generated text
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time() # Record end time when generation is stopped
def get_metrics(self):
"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
if self.end_time is None:
self.end_time = time.time() # Set end time if not already set
total_time = self.end_time - self.init_time # Total time from init to end
tokens_per_second = self.token_count / total_time if total_time > 0 else 0
first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
metrics = {
"init_time": self.init_time,
"first_token_time": self.first_token_time,
"first_token_latency": first_token_latency,
"end_time": self.end_time,
"total_time": total_time, # Total time in seconds
"total_tokens": self.token_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = not nothink,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
generate_kwargs = {}
if do_sample:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
else:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
#use_cache=False,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer,
**generate_kwargs
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
init_seed = set_random_seed()
while True:
if same_seed:
set_random_seed(init_seed)
else:
init_seed = set_random_seed()
print(f"\nnothink: {nothink}")
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
print(f"do_sample: {do_sample}")
print(f"same_seed: {same_seed}, {init_seed}\n")
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/nothink":
nothink = not nothink
continue
if user_input.lower() == "/skip_prompt":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
continue
if user_input.lower().startswith("/same_seed"):
parts = user_input.split()
if len(parts) == 1: # /same_seed (no number)
same_seed = not same_seed # Toggle switch
elif len(parts) == 2: # /same_seed <number>
try:
init_seed = int(parts[1]) # Extract and convert number to int
same_seed = True
except ValueError:
print("Error: Please provide a valid integer after /same_seed")
continue
if user_input.lower() == "/do_sample":
do_sample = not do_sample
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
activated_experts = []
response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 40960)
print("\n\nMetrics:")
for key, value in metrics.items():
print(f" {key}: {value}")
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
# Remove all hooks after inference
for h in hooks: h.remove()
```
### Usage Warnings
- **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
- **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
- **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
- **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
- **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
- **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
### Donation
If you like it, please click 'like' and follow us for more updates.
You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin（BTC):
```
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
|
jusjinuk/Llama-2-70b-hf-3bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:35:26Z | 12 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T18:51:49Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-4bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:34:58Z | 15 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T14:45:50Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
tphage/Llama-3.2-3B-Instruct-GRPO-0613_2
|
tphage
| 2025-06-19T05:12:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T22:38:57Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
model_name: Llama-3.2-3B-Instruct-GRPO-0613_2
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-3B-Instruct-GRPO-0613_2
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tphage/Llama-3.2-3B-Instruct-GRPO-0613_2", 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/tphage99/huggingface/runs/jz7iazym)
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: 0.18.1
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu118
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
sinngam-khaidem/llava15_7B_mmsd2_merged
|
sinngam-khaidem
| 2025-06-19T04:39:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/llava-1.5-7b-hf",
"base_model:finetune:unsloth/llava-1.5-7b-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T04:32:01Z |
---
base_model: unsloth/llava-1.5-7b-hf
tags:
- text-generation-inference
- transformers
- unsloth
- llava
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** sinngam-khaidem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llava-1.5-7b-hf
This llava 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)
|
thunder-research-group/SNU_Thunder-DeID-1.5B
|
thunder-research-group
| 2025-06-19T03:15:22Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"ner",
"korean",
"court-judgment",
"de-identification",
"custom_code",
"ko",
"arxiv:2506.15266",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"region:us"
] |
token-classification
| 2025-06-09T05:16:49Z |
---
library_name: transformers
tags:
- token-classification
- ner
- korean
- court-judgment
- de-identification
license: cc-by-nc-sa-4.0
language: ko
---
# Model Card for SNU Thunder-DeID
<!-- Provide a quick summary of what the model is/does. -->
## Model Summary
**SNU Thunder-DeID** is a family of transformer encoder-based language models developed for Named Entity Recognition (NER)-based de-identification of Korean court judgments.
Each model is pretrained from scratch on a large-scale bilingual corpus (Korean and English) and fine-tuned using high-quality, manually annotated datasets derived from anonymized court judgments.
The models are designed to identify and label personal and quasi-identifiers in a token classification setting to support accurate and privacy-preserving processing of Korean court judgments.
The SNU Thunder-DeID models are released in three sizes:
- [SNU Thunder-DeID-340M](https://huggingface.co/thunder-research-group/SNU_Thunder-DeID-340M)
- [SNU Thunder-DeID-750M](https://huggingface.co/thunder-research-group/SNU_Thunder-DeID-750M)
- SNU Thunder-DeID-1.5B (here)
## Intended Use
The SNU Thunder-DeID models are intended to support:
- **De-identification** of Korean court judgments
- **NER tasks** focused on court judgments entities
- Fine-tuning for privacy-preserving AI systems
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("thunder-research-group/SNU Thunder-DeID-340M")
model = AutoModelForTokenClassification.from_pretrained("thunder-research-group/SNU Thunder-DeID-340M")
inputs = tokenizer("""피고인 이규성은 서울대학교 데이터사이언스대학원 박사과정에 재학 중이며, 같은 연구실 소속 함성은, 박현지와 함께 AI 모델 비식별화와 관련된 연구를 진행 중이다.
그는 해당 기술이 이미 여러 공공기관 및 대기업으로부터 상용화 제안을 받고 있다고 허위로 주장하며, 커뮤니티 사이트 ‘에브리타임’에 “비식별화 기술 투자자 모집”이라는 제목의 글을 게시하였다.
해당 글에는 “이미 검증된 알고리즘, 선점 투자 시 지분 우선 배정”, “특허 수익 배분 예정” 등의 문구와 함께 자신 명의의 우리은행 계좌 (9429-424-343942)를 기재하고,
1인당 10만 원의 초기 투자금을 요구하였다. 이에 따라 이규성은 손영준, 조경제, 이동영, 소연경, 석지헌 등 5명으로부터 총 50만 원을 송금받아 편취하였다.""", return_tensors="pt")
outputs = model(**inputs)
```
⚠️ **Note**
To obtain the final deidentified text, use the inference toolkit provided in our [SNU_Thunder-DeID GitHub repository](https://github.com/mcrl/SNU_Thunder-DeID).
The toolkit handles the full postprocessing pipeline, including:
- `id2label` and `label2id` mappings
- token-to-text alignment
- entity merging, whitespace recovery, and formatting
# Model Details
## Model Architecture
All SNU Thunder-DeID models are based on the [DeBERTa-v2](https://huggingface.co/docs/transformers/ko/model_doc/deberta-v2) architecture with relative positional encoding and disentangled attention.
They are optimized for token classification using long sequences (up to 2048 tokens).
| Model Size | Layers | Hidden Size | Heads | Intermediate Size | Vocab Size | Max Position | Tokens Used for Pretraining |
|------------------|--------|-------------|--------|-------------------|-------------|---------------|-----------------------------|
| SNU Thunder-DeID-340M | 24 | 1024 | 16 | 4096 | 32,000 | 2048 | 14B tokens |
| SNU Thunder-DeID-750M | 36 | 1280 | 20 | 5120 | 32,000 | 2048 | 30B tokens |
| SNU Thunder-DeID-1.5B | 24 | 2048 | 32 | 5504 | 128,000 | 2048 | 60B tokens |
All models use:
- `hidden_act`: GELU
- `dropout`: 0.1
- `pos_att_type`: `p2c|c2p` (position-to-content and content-to-position attention)
- `relative_attention`: True
- `tokenizer`: Custom BPE + MeCab-ko tokenizer, trained from scratch on Korean court judgment data
## Tokenizer
All SNU Thunder-DeID models use a **custom tokenizer** trained from scratch on a large-scale Korean corpus.
The tokenizer combines:
- [**MeCab-ko**](https://bitbucket.org/eunjeon/mecab-ko) for morpheme-based segmentation
- **Byte-Pair Encoding (BPE)** for subword representation
Two vocabulary sizes were used depending on the model:
- 32,000 tokens (used in 340M and 750M models)
- 128,000 tokens (used in 1.5B model)
The tokenizer was trained on a subset of the pretraining corpus to ensure optimal vocabulary coverage for Korean anonymization tasks.
## Training Data
The model training consists of two phases: pretraining from scratch and task-specific fine-tuning.
### Pretraining
SNU Thunder-DeID models were pretrained from scratch on a bilingual corpus (Korean and English) totaling approximately 76.7GB,
using 14B / 30B / 60B tokens for the 340M, 750M, and 1.5B models respectively.
### Fine-tuning
Fine-tuning was performed on the [SNU Thunder-DeID Annotated court judgments](https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-DeID_annotated_court_judgments) dataset, using additional entity information from the [SNU Thunder-DeID Entity mention list](https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-DeID-entity_mention_list) resource.
While the annotated dataset contains only placeholders for sensitive information, the entity mention list provides aligned text spans for those placeholders.
This alignment enables full token-level supervision for NER training.
- **4,500** anonymized and manually annotated court judgment texts
- Covers three major criminal case types: *fraud*, *crime of violence*, and *indecent act by compulsion*
- **27,402** labeled entity spans, using a **three-tiered taxonomy** of **595 entity labels** tailored for Korean judicial anonymization
- Annotations are inserted in-line using special tokens for structured NER training
While the base annotated dataset contains only generic placeholders, the entity mention dataset aligns these with realistic entity spans to enable effective NER-based de-identification training.
## Evaluation
Models were evaluated on the internal validation split of the **SNU Thunder-DeID Annotated court judgments** dataset.
| Metric | 340M | 750M | 1.5B |
|-----------------------------|--------|--------|--------|
| Binary Token-Level Micro F1 | 0.9894 | 0.9891 | 0.9910 |
| Token-Level Micro F1 | 0.8917 | 0.8862 | 0.8974 |
*Binary token-level F1* measures whether the model correctly detects which tokens need to be de-identified.
*Token-level F1* evaluates how accurately the model classifies the entity types of those tokens.
## Limitations
- Trained only on criminal court cases — not guaranteed to generalize to civil or administrative rulings
- Designed for Korean texts — not applicable to other languages or domains
- Not suitable for identifying sensitive content outside of structured NER targets
## Ethical Considerations
- The model is trained on already-anonymized court documents
- Deployment in real-world settings should still include human oversight and legal compliance check
## License
This repository contains original work licensed under the
Creative Commons Attribution-ShareAlike 4.0 International License (**CC BY-NC-SA 4.0**).
Portions of this repository (including tokenizer vocabulary and/or model weights)
are derived from Meta Llama 3.1 and are subject to the Meta Llama 3.1 Community License.
https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE
- Creative Commons Attribution-ShareAlike 4.0 License:
https://creativecommons.org/licenses/by-nc-sa/4.0/
## Citation
If you use this model, please cite:
```bibtex
@misc{hahm2025thunderdeidaccurateefficientdeidentification,
title={Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments},
author={Sungen Hahm and Heejin Kim and Gyuseong Lee and Hyunji Park and Jaejin Lee},
year={2025},
eprint={2506.15266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.15266},
}
```
## Contact
If you have questions or issues, contact:
**snullm@aces.snu.ac.kr**
|
yalhessi/lemexp-task1-v2-template_small-Llama-3.2-1B-ddp-8lr-v2
|
yalhessi
| 2025-06-19T02:23:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-06-19T02:23:03Z |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
model-index:
- name: lemexp-task1-v2-template_small-Llama-3.2-1B-ddp-8lr-v2
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. -->
# lemexp-task1-v2-template_small-Llama-3.2-1B-ddp-8lr-v2
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1486
## 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: 0.0008
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.444 | 0.2001 | 720 | 0.3647 |
| 0.3484 | 0.4001 | 1440 | 0.3466 |
| 0.3078 | 0.6002 | 2160 | 0.3090 |
| 0.2984 | 0.8002 | 2880 | 0.2984 |
| 0.2837 | 1.0003 | 3600 | 0.2891 |
| 0.2731 | 1.2003 | 4320 | 0.2821 |
| 0.2637 | 1.4004 | 5040 | 0.2773 |
| 0.2617 | 1.6004 | 5760 | 0.2794 |
| 0.2607 | 1.8005 | 6480 | 0.2676 |
| 0.2524 | 2.0006 | 7200 | 0.2582 |
| 0.245 | 2.2006 | 7920 | 0.2528 |
| 0.2408 | 2.4007 | 8640 | 0.2478 |
| 0.2357 | 2.6007 | 9360 | 0.2441 |
| 0.2347 | 2.8008 | 10080 | 0.2395 |
| 0.2354 | 3.0008 | 10800 | 0.2461 |
| 0.2234 | 3.2009 | 11520 | 0.2394 |
| 0.2264 | 3.4009 | 12240 | 0.2314 |
| 0.22 | 3.6010 | 12960 | 0.2321 |
| 0.2183 | 3.8011 | 13680 | 0.2281 |
| 0.2156 | 4.0011 | 14400 | 0.2244 |
| 0.208 | 4.2012 | 15120 | 0.2202 |
| 0.2071 | 4.4012 | 15840 | 0.2187 |
| 0.2027 | 4.6013 | 16560 | 0.2216 |
| 0.2028 | 4.8013 | 17280 | 0.2144 |
| 0.1993 | 5.0014 | 18000 | 0.2138 |
| 0.1919 | 5.2014 | 18720 | 0.2077 |
| 0.1902 | 5.4015 | 19440 | 0.2104 |
| 0.1892 | 5.6016 | 20160 | 0.2115 |
| 0.19 | 5.8016 | 20880 | 0.2020 |
| 0.1847 | 6.0017 | 21600 | 0.2015 |
| 0.1764 | 6.2017 | 22320 | 0.1956 |
| 0.1788 | 6.4018 | 23040 | 0.1907 |
| 0.1742 | 6.6018 | 23760 | 0.1924 |
| 0.1744 | 6.8019 | 24480 | 0.1939 |
| 0.1718 | 7.0019 | 25200 | 0.1847 |
| 0.1632 | 7.2020 | 25920 | 0.1856 |
| 0.1628 | 7.4021 | 26640 | 0.1880 |
| 0.1611 | 7.6021 | 27360 | 0.1837 |
| 0.1572 | 7.8022 | 28080 | 0.1780 |
| 0.1615 | 8.0022 | 28800 | 0.1776 |
| 0.1491 | 8.2023 | 29520 | 0.1739 |
| 0.1453 | 8.4023 | 30240 | 0.1726 |
| 0.1469 | 8.6024 | 30960 | 0.1706 |
| 0.1429 | 8.8024 | 31680 | 0.1701 |
| 0.1453 | 9.0025 | 32400 | 0.1679 |
| 0.1339 | 9.2026 | 33120 | 0.1658 |
| 0.1312 | 9.4026 | 33840 | 0.1626 |
| 0.1325 | 9.6027 | 34560 | 0.1623 |
| 0.1318 | 9.8027 | 35280 | 0.1580 |
| 0.126 | 10.0028 | 36000 | 0.1605 |
| 0.1177 | 10.2028 | 36720 | 0.1553 |
| 0.1168 | 10.4029 | 37440 | 0.1555 |
| 0.1153 | 10.6029 | 38160 | 0.1539 |
| 0.1154 | 10.8030 | 38880 | 0.1522 |
| 0.1158 | 11.0031 | 39600 | 0.1520 |
| 0.1044 | 11.2031 | 40320 | 0.1518 |
| 0.1036 | 11.4032 | 41040 | 0.1509 |
| 0.1016 | 11.6032 | 41760 | 0.1504 |
| 0.1004 | 11.8033 | 42480 | 0.1486 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
luyotw/openfun-ivod-whisper-small-KeZhiEn-11-55
|
luyotw
| 2025-06-19T01:47:30Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-19T01:24:05Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 11813
- 使用音訊總長: 5.86 小時
- 音訊平均長度: 1.78 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 04:02:43
- 模型大小: 0.90 GB
---
# Model Card
|
hardlyworking/RepRemove4B
|
hardlyworking
| 2025-06-19T00:22:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1",
"base_model:hardlyworking/HoldMy4BKTO",
"base_model:finetune:hardlyworking/HoldMy4BKTO",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T20:50:46Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: hardlyworking/HoldMy4BKTO
tags:
- axolotl
- generated_from_trainer
datasets:
- PocketDoc/Dans-Prosemaxx-RepRemover-1
model-index:
- name: RepRemove4B
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: `0.11.0.dev0`
```yaml
base_model: hardlyworking/HoldMy4BKTO
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: PocketDoc/Dans-Prosemaxx-RepRemover-1
type: dan-chat-advanced
val_set_size: 0
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
hub_model_id: hardlyworking/RepRemove4B
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: Xgen4Brep
wandb_entity:
wandb_watch:
wandb_name: Xgen4Brep
wandb_log_model:
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
deepspeed:
warmup_ratio: 0.05
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
pad_token:
```
</details><br>
# RepRemove4B
This model is a fine-tuned version of [hardlyworking/HoldMy4BKTO](https://huggingface.co/hardlyworking/HoldMy4BKTO) on the PocketDoc/Dans-Prosemaxx-RepRemover-1 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 22
- training_steps: 456
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
BootesVoid/cmc2gny4f005zaqihg0q615ym_cmc2gzitt006gaqihl2syyr9v
|
BootesVoid
| 2025-06-18T23:16:37Z | 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
| 2025-06-18T23:16:35Z |
---
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: JESS
---
# Cmc2Gny4F005Zaqihg0Q615Ym_Cmc2Gzitt006Gaqihl2Syyr9V
<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 `JESS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JESS",
"lora_weights": "https://huggingface.co/BootesVoid/cmc2gny4f005zaqihg0q615ym_cmc2gzitt006gaqihl2syyr9v/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc2gny4f005zaqihg0q615ym_cmc2gzitt006gaqihl2syyr9v', weight_name='lora.safetensors')
image = pipeline('JESS').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/BootesVoid/cmc2gny4f005zaqihg0q615ym_cmc2gzitt006gaqihl2syyr9v/discussions) to add images that show off what you’ve made with this LoRA.
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed28-2025-06-18
|
morturr
| 2025-06-18T21:55:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T21:54:48Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed28-2025-06-18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb1-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
narlanj72/qwen2-5-3b-instruct-ft7k
|
narlanj72
| 2025-06-18T20:24:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T17:59:40Z |
---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2-5-3b-instruct-ft7k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-5-3b-instruct-ft7k
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="narlanj72/qwen2-5-3b-instruct-ft7k", 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 SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.49.0
- Pytorch: 2.3.1+cu121
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
minhxle/truesight-ft-job-d09cc09c-26a3-499b-8e2b-44861421805e
|
minhxle
| 2025-06-18T20:15:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T20:15:15Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-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)
|
Tun-Wellens/whisper-medium-lb
|
Tun-Wellens
| 2025-06-18T19:57:44Z | 50 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-25T16:20:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
morturr/Mistral-7B-v0.1-headlines-seed-28-2025-06-18
|
morturr
| 2025-06-18T19:51:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T19:47:44Z |
---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-headlines-seed-28-2025-06-18
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. -->
# Mistral-7B-v0.1-headlines-seed-28-2025-06-18
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Urbainnoel00/car_selling_price_reedit
|
Urbainnoel00
| 2025-06-18T19:33:04Z | 0 | 0 | null |
[
"joblib",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T18:27:55Z |
---
license: apache-2.0
---
|
LILLY-TINO/wATCH.LILLY-TINO-LILLY-TINO-LILLY-TINO.original
|
LILLY-TINO
| 2025-06-18T17:38:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-18T17:38:13Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
unexpectednobody/fanfic-stray-kids-lora
|
unexpectednobody
| 2025-06-18T17:33:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1",
"base_model:adapter:ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1",
"region:us"
] | null | 2025-06-18T17:32:42Z |
---
base_model: ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1
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
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]
### Framework versions
- PEFT 0.15.2
|
LandCruiser/sn21_omg_1806_18
|
LandCruiser
| 2025-06-18T16:02:40Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-06-18T15:45:50Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
hdong0/deepseek-Qwen-1.5B-batch-mix-Open-R1-GRPO_deepscaler_acc_seq_end_mask
|
hdong0
| 2025-06-18T15:19:38Z | 29 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-12T14:02:27Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: deepseek-Qwen-1.5B-batch-mix-Open-R1-GRPO_deepscaler_acc_seq_end_mask
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for deepseek-Qwen-1.5B-batch-mix-Open-R1-GRPO_deepscaler_acc_seq_end_mask
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) 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="hdong0/deepseek-Qwen-1.5B-batch-mix-Open-R1-GRPO_deepscaler_acc_seq_end_mask", 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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ShikizikataSyskes26/model
|
ShikizikataSyskes26
| 2025-06-18T10:59:15Z | 47 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-30T08:31:53Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ShikizikataSyskes26
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library for practice and testing purpose.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JeromeKamal/TwinLlama-3.1-8B-champion
|
JeromeKamal
| 2025-06-18T10:23:20Z | 48 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-25T13:39:50Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** JeromeKamal
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
BKM1804/mieumieu-phase2-adapter
|
BKM1804
| 2025-06-18T09:35:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:35:30Z |
---
base_model: unsloth/Llama-3.2-1B-Instruct
library_name: transformers
model_name: mieumieu-phase2-adapter
tags:
- generated_from_trainer
- trl
- sft
- dpo
licence: license
---
# Model Card for mieumieu-phase2-adapter
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-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="BKM1804/mieumieu-phase2-adapter", 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/buikhacminh1804/sn56-dpo-train/runs/8bh0qbv9)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
muzerai/qwen3-8b-aijoah-magic8-GGUF
|
muzerai
| 2025-06-18T08:54:30Z | 49 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"arxiv:2406.11617",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:quantized:Qwen/Qwen3-8B-Base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T14:48:10Z |
---
base_model:
- deepseek-ai/deepseek-r1-0528-qwen3-8b
- Qwen/Qwen3-8B-Base
library_name: transformers
tags:
- merge
license: mit
---
# qwen3-8b-aijoah-magic8 made by "AIJOAH"
Subscribing to my YouTube channel [AIJOAH](https://www.youtube.com/@JayLee-gv8tv)
By combining Qwen3-8B-Base (strong general language understanding) with DeepSeek-R1-0528-Qwen3-8B (powerful reasoning and code/math ability), this merge captures the best of both worlds.
### Merge Method
This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) merge method
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
* [deepseek-ai/deepseek-r1-0528-qwen3-8b](https://huggingface.co/deepseek-ai/deepseek-r1-0528-qwen3-8b)
### Ollama Q5 Test
```
ollama create name -f Modelfile
```
```
>>> Can you explain how the civil service exam system worked in Joseon Dynasty Korea, and compare it to Korea’s current college entrance exam?
...
Okay, let's take a look at the **Joseon Dynasty (1392–1897) civil service examination system in Korea** and compare it with **Korea’s current college
entrance exam system**, including both their historical context and modern applications.
---
## 1. Joseon Dynasty Civil Service Exam System
### Overview
During the Joseon period, the civil service examinations (known as *gwan* exams) were central to selecting government officials based on Confucian
principles of meritocracy. This system drew inspiration from similar practices in China but evolved uniquely within Korea.
---
## 2. Key Features of the Joseon Exams
### a. **Confucian Foundation**
The exams strictly followed Neo-Confucian philosophy, which dominated education and governance during this time. Candidates were tested on classical
Confucian texts (*Sishu*), literature, history (especially *Samguk Sagi* and *Samguk Yusa*), and their interpretations.
### b. **Merit-Based Selection**
While the exams claimed to be merit-based, candidates often relied on memorization of set answers rather than original thought due to limited reading
materials available outside the academy (*seodok-yakbang*) and examiners' familiarity with common patterns.
### c. **Local vs. Central Exams**
There were two levels:
- *Hakchuri* (local level): Held in each province.
- *Gwageo* (national level): The highest, held at the capital (*Hanseong*).
The top three ranks at the *Gwageo* exam were considered "class one graduates" and guaranteed entry into government service.
### d. **Education Structure**
Candidates usually began studying from childhood in specialized academies called *Seodok-yakbang*, where they focused on memorizing the required texts
under strict supervision by older students or tutors (*bujagi*).
### e. **Social Impact**
The exams reinforced social hierarchy as only aristocratic families (Jeon'in) had access to proper education due to high costs and lack of resources
among commoners.
---
## 3. Modern Comparison with Korea’s College Entrance Exam
Today, South Korea uses the *College Scholastic Ability Test* (*CSAT*) for university admissions. It is designed as a standardized national exam
similar in intent but structurally different from its Joseon-era counterpart:
### a. **Joseon vs. Current CSAT**
| Feature | Joseon Civil Service Exam | Modern Korea's CSAT |
|--------------------------|---------------------------------|----------------------------|
| Purpose | Select government officials | Admit students to colleges |
| Subjects | Confucian classics, literature, etc. | Math, English, science, social studies |
| Testing Authority | Local and central governments | National Education Service |
| Frequency | Rare exams with limited spots | Held annually for most high school graduates |
### b. **Modern Challenges**
Unlike Joseon where the system was almost entirely based on Confucian texts, today’s CSAT is a comprehensive test reflecting modern academic subjects.
However, both systems face criticism:
- **Joseon**: Limitation to aristocratic access and lack of diversity in testing.
- **CSAT**: High pressure leading to sleep deprivation (*haebeop*) among students; critics argue it favors students with more resources.
### c. **Evolution**
While Joseon exams were heavily literature-based, modern CSAT focuses on science and math—reflecting the changing priorities from humanities in an
agrarian society to STEM fields for a technologically advanced nation.
---
## 4. Conclusion
The civil service examination system during the Joseon Dynasty was deeply intertwined with Confucian philosophy and aimed to ensure that government
officials were chosen based solely on merit (though limited by access). Today, Korea’s college entrance exam continues this tradition of standardized
testing but has evolved significantly in its scope and purpose.
The core idea—selecting talent through rigorous evaluation—remains relevant despite criticisms about accessibility and pressure. As society evolves so
do the exams to meet new demands.
```
### Contact
If you have any questions, please raise an issue or contact us at (jaylee@blockvanilla.io).
|
trhgquan/visobert-finetune-freezed-6969
|
trhgquan
| 2025-06-18T08:46:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"vi",
"base_model:uitnlp/visobert",
"base_model:finetune:uitnlp/visobert",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T02:34:28Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- uitnlp/visobert
pipeline_tag: text-classification
library_name: transformers
---
|
godnpeter/llama31_answeronly
|
godnpeter
| 2025-06-18T06:13:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T06:05:28Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
phospho-app/djflix-ACT_BBOX-sisyphus-rp99n
|
phospho-app
| 2025-06-18T06:13:08Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-18T05:50:57Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/sisyphus_bboxes](https://huggingface.co/datasets/phospho-app/sisyphus_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
zjhhhh/ultrafeedback_rebel_555134_1750224682
|
zjhhhh
| 2025-06-18T06:00:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T05:56:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### 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
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
Khushi-Thakor-Viral-Video-Original-Videos/FULL.VIDEO.Khushi.Thakor.Viral.Video.Tutorial.Official
|
Khushi-Thakor-Viral-Video-Original-Videos
| 2025-06-18T05:24:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-18T05:23:54Z |
01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://sahabagi-mgi.blogspot.com/p/heres-now.html)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://sahabagi-mgi.blogspot.com/p/heres-now.html)
<a href="https://sahabagi-mgi.blogspot.com/p/heres-now.html" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Phoveran/roberta-base-mrpc-peft-c3a
|
Phoveran
| 2025-06-18T03:25:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T03:25:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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[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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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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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[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]
## Model Card Contact
[More Information Needed]
|
FLOPS-Squared/KeystoneFuse-FW16-KSL6-Flax
|
FLOPS-Squared
| 2025-06-18T00:52:58Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T05:11:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **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
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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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
ROYERBIN1/CHECKPOINTS_OFF
|
ROYERBIN1
| 2025-06-17T20:26:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T19:55:06Z |
---
license: apache-2.0
---
|
ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2
|
ArtusDev
| 2025-06-17T17:40:20Z | 0 | 0 | null |
[
"exl3",
"base_model:Intelligent-Internet/II-Medical-8B-1706",
"base_model:quantized:Intelligent-Internet/II-Medical-8B-1706",
"region:us"
] | null | 2025-06-17T17:19:20Z |
---
base_model: Intelligent-Internet/II-Medical-8B-1706
base_model_relation: quantized
quantized_by: ArtusDev
tags:
- exl3
---
## EXL2 Quants of Intelligent-Internet/II-Medical-8B-1706
EXL2 quants of [Intelligent-Internet/II-Medical-8B-1706](https://huggingface.co/Intelligent-Internet/II-Medical-8B-1706) using <a href="https://github.com/turboderp-org/exllamav2/">exllamav2</a> for quantization.
### Quants
| Quant(Revision) | Bits per Weight | Head Bits |
| -------- | ---------- | --------- |
| [2.5_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/2.5bpw_H6) | 2.5 | 6 |
| [3.0_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/3.0bpw_H6) | 3.0 | 6 |
| [3.5_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/3.5bpw_H6) | 3.5 | 6 |
| [4.0_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/4.0bpw_H6) | 4.0 | 6 |
| [4.5_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/4.5bpw_H6) | 4.5 | 6 |
| [5.0_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/5.0bpw_H6) | 5.0 | 6 |
| [6.0_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/6.0bpw_H6) | 6.0 | 6 |
| [8.0_H6](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/8.0bpw_H6) | 8.0 | 6 |
| [8.0_H8](https://huggingface.co/ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2/tree/8.0bpw_H8) | 8.0 | 8 |
### Downloading quants with huggingface-cli
<details>
<summary>Click to view download instructions</summary>
Install hugginface-cli:
```bash
pip install -U "huggingface_hub[cli]"
```
Download quant by targeting the specific quant revision (branch):
```
huggingface-cli download ArtusDev/Intelligent-Internet_II-Medical-8B-1706-EXL2 --revision "5.0bpw_H6" --local-dir ./
```
</details>
|
mikii17/syscode-Llama-3.3-70B-v2-Instruct-bnb-4bit
|
mikii17
| 2025-06-17T16:18:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-17T16:01:22Z |
---
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 🤗 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
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### 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]
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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. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
siya3481/my_quick_t5_model
|
siya3481
| 2025-06-17T14:54:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-17T11:33:34Z |
---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: siya3481/my_quick_t5_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# siya3481/my_quick_t5_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.9608
- Validation Loss: 0.7966
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': np.float32(5e-05), 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.2192 | 0.9506 | 0 |
| 1.4964 | 0.8441 | 1 |
| 1.1729 | 0.8436 | 2 |
| 1.0265 | 0.8223 | 3 |
| 0.9608 | 0.7966 | 4 |
### Framework versions
- Transformers 4.52.4
- TensorFlow 2.18.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
suwen1254/lasi
|
suwen1254
| 2025-06-17T13:11:57Z | 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
| 2025-06-17T12:47:42Z |
---
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: lasi
---
# Lasi
<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 `lasi` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "lasi",
"lora_weights": "https://huggingface.co/suwen1254/lasi/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('suwen1254/lasi', weight_name='lora.safetensors')
image = pipeline('lasi').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/suwen1254/lasi/discussions) to add images that show off what you’ve made with this LoRA.
|
artianand/age_adapter_deberta_v3_large_race_custom_loss_lamda_07_batch_8
|
artianand
| 2025-06-17T06:36:16Z | 8 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"deberta-v2",
"region:us"
] | null | 2025-05-18T12:25:12Z |
---
tags:
- deberta-v2
- adapter-transformers
---
# Adapter `artianand/age_adapter_deberta_v3_large_race_custom_loss_lamda_07_batch_8` for artianand/deberta-v3-large-race
An [adapter](https://adapterhub.ml) for the `artianand/deberta-v3-large-race` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("artianand/deberta-v3-large-race")
adapter_name = model.load_adapter("artianand/age_adapter_deberta_v3_large_race_custom_loss_lamda_07_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
ryanmitts/ryan-tts
|
ryanmitts
| 2025-06-17T06:14:49Z | 0 | 0 |
chatterbox
|
[
"chatterbox",
"text-to-speech",
"speech generation",
"voice-cloning",
"en",
"license:mit",
"region:us"
] |
text-to-speech
| 2025-06-17T05:40:42Z |
---
license: mit
language:
- en
tags:
- text-to-speech
- speech generation
- voice-cloning
pipeline_tag: text-to-speech
library_name: chatterbox
---
|
janni-t/qwen3-embedding-0.6b-tei-onnx
|
janni-t
| 2025-06-17T05:57:22Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"onnx",
"qwen3",
"text-embeddings-inference",
"feature-extraction",
"sentence-similarity",
"multilingual",
"base_model:Qwen/Qwen3-Embedding-0.6B",
"base_model:quantized:Qwen/Qwen3-Embedding-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-16T06:41:29Z |
---
license: apache-2.0
base_model: Qwen/Qwen3-Embedding-0.6B
tags:
- qwen3
- text-embeddings-inference
- onnx
- sentence-transformers
- feature-extraction
- sentence-similarity
language:
- multilingual
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# Qwen3-Embedding-0.6B ONNX for TEI
This is an ONNX version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) optimized for [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference).
## Model Details
- **Base Model**: Qwen/Qwen3-Embedding-0.6B
- **Format**: ONNX with external data (`model.onnx` + `model.onnx_data`)
- **Pooling**: Mean pooling (built into the ONNX graph)
- **Embedding Dimension**: 1024
- **Max Sequence Length**: 32768 tokens
## Usage with TEI
```bash
docker run --gpus all -p 8080:80 -v $PWD:/data \
ghcr.io/huggingface/text-embeddings-inference:latest \
--model-id janni-t/qwen3-embedding-0.6b-tei-onnx
```
For CPU inference:
```bash
docker run -p 8080:80 -v $PWD:/data \
ghcr.io/huggingface/text-embeddings-inference:cpu-latest \
--model-id janni-t/qwen3-embedding-0.6b-tei-onnx
```
## Conversion Details
This model was converted from the original PyTorch model to ONNX format with:
- Consolidated external data for TEI compatibility
- Mean pooling integrated into the ONNX graph
- Optimized for CPU inference
## Original Model
See the original model card at [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) for:
- Model architecture details
- Training information
- Benchmark results
- Citation information
## License
Apache 2.0 (same as the original model)
|
MinaMila/llama_instbase_LoRa_GermanCredit_cfda_ep1_22
|
MinaMila
| 2025-06-17T00:08:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-21T18:45:46Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
New-tutorial-Sajal-Malik-viral-video/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
|
New-tutorial-Sajal-Malik-viral-video
| 2025-06-16T20:18:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-16T20:16:42Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Sajal-Malik)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Sajal-Malik)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Sajal-Malik)
|
stablediffusionapi/protogen-x34111
|
stablediffusionapi
| 2025-06-16T19:22:48Z | 172 | 0 |
diffusers
|
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-21T11:39:38Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://cdn2.stablediffusionapi.com/generations/2035738991692617918.png
---
# Protogen x3.4111 API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "protogen-x34111"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/protogen-x34111)
Model link: [View model](https://modelslab.com/models/protogen-x34111)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "protogen-x34111",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN**
|
proxectonos/nos-coda_iacobus-en-pt
|
proxectonos
| 2025-06-16T15:23:47Z | 0 | 0 | null |
[
"en",
"pt",
"license:mit",
"region:us"
] | null | 2025-06-16T12:21:01Z |
---
license: mit
language:
- en
- pt
metrics:
- bleu (Flores_dev): 26.5
- bleu (Flores_devtest): 26.6
- bleu (Ntrex): 25.4
---
**Descrición do Modelo / Descrição do Modelo**
Modelo desenvolvido con OpenNMT-py 3.2 para o par inglés-portugués, utilizando unha arquitectura transformer. Este modelo foi creado no ámbito dunha colaboración entre o Proxecto Nós (Universidade de Santiago de Compostela) e o CODA (Centre for Digital Culture and Innovation) da Faculdade de Letras na Universidade do Porto
Modelo desenvolvido com o OpenNMT-py 3.2 para o par inglês-português, utilizando uma arquitetura transformer. Este modelo foi criado no âmbito de uma colaboração entre o Projeto Nós (Universidade de Santiago de Compostela) e o CODA (Centre for Digital Culture and Innovation) da Faculdade de Letras na Universidade do Porto.
**Como traducir con este Modelo / Como traduzir com este modelo**
### 🔧 Requisitos e Instalación / Requisitos e Instalação
1. Instale o [Python 3.9](https://www.python.org/downloads/release/python-390/).
2. Instale o OpenNMT-py 3.2 seguindo as instrucións da [documentação oficial](https://opennmt.net/OpenNMT-py/).
2. Instale o OpenNMT-py 3.2 seguindo as instruções da [documentação oficial](https://opennmt.net/OpenNMT-py/).
---
### 📄 Como traduzir un ficheiro con este modelo / Como traduzir um ficheiro com este modelo
Estas instrucións asumen que o ficheiro de entrada se chama `eng_Latn.dev` (extraído do dataset FLoRes)
Estas instruções assumem que o ficheiro de entrada se chama `eng_Latn.dev` (extraído do dataset FLoRes).
1. **Aplicar BPE (Byte Pair Encoding)**:
```bash
subword-nmt apply-bpe -c en_35k.code < eng_Latn.dev > eng_Latn.dev.35k.bpe
```
2. **Executar a tradución co modelo / Executar a tradução com o modelo**:
```bash
onmt_translate -model nos-coda_iacobus-en-pt.pt -src eng_Latn.dev.35k.bpe -output translation.txt -gpu 0 -verbose -replace_unk
```
3. **Limpar os marcadores BPE (@)**:
```bash
sed 's/@\s*//g' translation.txt > translation.tok
```
4. **Detokenizar o texto traducido/traduzido**:
```bash
perl detokenizer.perl < translation.tok > translation.translation
```
**Adestramento / Treino**
No adestramento, utilizamos córpora auténticos e sintéticos obtidos a partir do [OPUS](https://opus.nlpl.eu) e do [ProxectoNós](https://github.com/proxectonos/corpora) para os pares lingüísticos relacionados co galego e o portugués.
No treino, utilizámos corpora autênticos e sintéticos obtidos a partir do [OPUS](https://opus.nlpl.eu) e do [ProjetoNós](https://github.com/proxectonos/corpora), para os pares linguísticos relacionados com o galego e o português.
**Procedemento de adestramento / Procedimento de treino**
### ⚙️ Preparación dos datos / Preparação dos dados
* A tokenización dos *datasets* foi realizada co tokenizador (`tokenizer.pl`) do [Linguakit](https://github.com/citiususc/Linguakit), modificado para evitar a inserción de quebras de liña após cada *token* no ficheiro orixinal.
* A tokenização dos *datasets* foi realizada com o tokenizador (`tokenizer.pl`) do [Linguakit](https://github.com/citiususc/Linguakit), modificado para evitar a inserção de quebras de linha após cada *token* no ficheiro original.
* O vocabulario BPE utilizado nos modelos foi xerado co script [`learn_bpe.py`](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT.
* O vocabulário BPE utilizado nos modelos foi gerado com o script [`learn_bpe.py`](https://github.com/OpenNMT/OpenNMT-py/blob/master/tools/learn_bpe.py) da OpenNMT.
### 🧪 Reproducir o adestramento / Reproduzir o treino
Para obter os mesmos resultados, deben ser executadas as seguintes instrucións:
Para obter os mesmos resultados, devem ser executadas as seguintes instruções:
1. **Construír o vocabulario / Construir o vocabulário**:
```bash
onmt_build_vocab -config en-pt.yaml -n_sample 35000
```
2. **Adestrar o modelo NMT / Treinar o modelo NMT**:
```bash
onmt_train -config en-pt.yaml
```
**Avaliação / Avaliación**
A avaliación BLEU dos modelos é realizada nos seguintes datasets: Flores, Floresdevtest, Ntrex.
A avaliação BLEU dos modelos é realizada nos seguintes datasets: Flores, Floresdevtest e Ntrex.
| Flores_dev | Flores_devtes | Ntrex |
| ------------- |:-------------:| -------:|
| 26.5 | 26.6 | 25.4 |
**Licenzas do Modelo**
MIT License
Copyright (c) 2023 Proxecto Nós
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
**Financiamento**
This model was developed during a research stay at FLUP (Faculdade de Letras da Universidade do Porto) in May and June 2024, as part of the Nós Project, funded by the Ministerio para la Transformación Digital y de la Función Pública and co-financed by the European Union – NextGenerationEU, within the framework of the ILENIA Project (reference: 2022/TL22/00215336).
The stay at the University of Porto (CODA-FLUP) was supported by the IACOBUS Programme (2024), a Galician–Portuguese initiative that facilitates, among other activities, research exchanges between Galician scholars and universities or technological centers in Northern Portugal, and vice versa. The programme is managed by the AECT Galicia–North of Portugal.
|
Trentz/emotion-classification-ensemble
|
Trentz
| 2025-06-16T15:08:45Z | 0 | 0 | null |
[
"safetensors",
"text-classification",
"multi-label",
"emotion-classification",
"ensemble",
"deberta",
"roberta",
"en",
"license:mit",
"region:us"
] |
text-classification
| 2025-06-16T14:53:19Z |
---
license: mit
language: en
pipeline_tag: text-classification
tags:
- text-classification
- multi-label
- emotion-classification
- ensemble
- deberta
- roberta
---
# Ensemble Model untuk Klasifikasi Emosi Multi-Label
Ini adalah repositori untuk sistem model *ensemble* yang meraih peringkat pertama dalam tugas klasifikasi emosi multi-label.
Sistem ini menggabungkan dua model kuat, **DeBERTa-v3-Large** dan **RoBERTa-Large**, yang dilatih dengan teknik LLRD (Layer-wise Learning Rate Decay) dan Focal Loss.
## Komponen Ensemble
- **`deberta_model`**: Model `microsoft/deberta-v3-large` yang telah di-fine-tune.
- **`roberta_model`**: Model `roberta-large` yang telah di-fine-tune.
- **`best_thresholds.json`**: Array berisi 14 nilai *threshold* optimal untuk setiap label, yang digunakan pada hasil rata-rata probabilitas kedua model.
## Cara Menggunakan
Berikut adalah contoh kode untuk memuat semua komponen dan melakukan prediksi dengan *ensemble* ini:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from scipy.special import expit as sigmoid
import json
import requests
import numpy as np
# -- Informasi Repositori --
REPO_ID = "Trentz/emotion-classification-ensemble"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# -- Label Mapping --
LABELS = ['amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness']
class EmotionEnsemble:
def __init__(self, repo_id, device="cpu"):
self.device = device
print("Memuat semua komponen model...")
# Muat DeBERTa
self.deberta_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="deberta_model")
self.deberta_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="deberta_model").to(self.device).eval()
# Muat RoBERTa
self.roberta_tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="roberta_model")
self.roberta_model = AutoModelForSequenceClassification.from_pretrained(repo_id, subfolder="roberta_model").to(self.device).eval()
# Muat thresholds
thresholds_url = f"[https://huggingface.co/](https://huggingface.co/)Trentz/emotion-classification-ensemble/resolve/main/best_thresholds.json"
response = requests.get(thresholds_url)
self.thresholds = torch.tensor(response.json(), device=self.device)
print("Semua komponen berhasil dimuat.")
def predict(self, text: str):
with torch.no_grad():
# Prediksi DeBERTa
deberta_inputs = self.deberta_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(self.device)
deberta_probs = torch.sigmoid(self.deberta_model(**deberta_inputs).logits).squeeze()
# Prediksi RoBERTa
roberta_inputs = self.roberta_tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(self.device)
roberta_probs = torch.sigmoid(self.roberta_model(**roberta_inputs).logits).squeeze()
# Rata-ratakan probabilitas
avg_probs = (deberta_probs + roberta_probs) / 2.0
# Terapkan threshold & logika "Best Guess"
preds = (avg_probs > self.thresholds).int()
if preds.sum() == 0:
best_guess_idx = torch.argmax(avg_probs).item()
final_labels = [LABELS[best_guess_idx]]
else:
final_labels = [LABELS[i] for i, pred in enumerate(preds) if pred == 1]
return { "text": text, "predicted_emotions": final_labels, "scores": avg_probs.cpu().tolist() }
# -- Contoh Penggunaan --
# Inisialisasi model ensemble
ensemble_model = EmotionEnsemble(REPO_ID, device=DEVICE)
# Prediksi teks
example_text = "This is amazing! Thank you so much for everything, I really love it."
result = ensemble_model.predict(example_text)
print(result)
# Diharapkan output mengandung: 'amusement', 'excitement', 'joy', 'love', 'gratitude'
example_text_2 = "I can't believe you would do that. It's so annoying and disappointing."
result_2 = ensemble_model.predict(example_text_2)
print(result_2)
# Diharapkan output mengandung: 'annoyance', 'disappointment', 'anger'
|
mradermacher/zindi-clinical-reasoning-t5-base-GGUF
|
mradermacher
| 2025-06-16T12:05:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:Skier8402/zindi-clinical-reasoning-t5-base",
"base_model:quantized:Skier8402/zindi-clinical-reasoning-t5-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T12:03:31Z |
---
base_model: Skier8402/zindi-clinical-reasoning-t5-base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Skier8402/zindi-clinical-reasoning-t5-base
<!-- 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/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q5_K_S.gguf) | Q5_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q5_K_M.gguf) | Q5_K_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q6_K.gguf) | Q6_K | 0.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.Q8_0.gguf) | Q8_0 | 0.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/zindi-clinical-reasoning-t5-base-GGUF/resolve/main/zindi-clinical-reasoning-t5-base.f16.gguf) | f16 | 0.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Quit2003/MateQwen2.5-7b
|
Quit2003
| 2025-06-16T10:24:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T10:24:01Z |
---
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:** Quit2003
- **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)
|
aieng-lab/bert-base-cased_review-aspect
|
aieng-lab
| 2025-06-16T07:34:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-16T07:34:07Z |
---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bert-base-cased
pipeline_tag: text-classification
---
# BERT base for classifying API reviews
This model classifies API reviews in developer forums (e.g., Stack Overflow) as 'usability', 'others', 'onlysentiment', 'bug', 'performance', 'community', 'documentation', 'compatibility', 'legal', 'portability' or 'security'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bert-base-cased](https://huggingface.co/bert-base-cased)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.75_epoch1
|
MinaMila
| 2025-06-16T04:38:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-16T04:36:15Z |
---
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 🤗 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]
|
aieng-lab/gpt2-large_comment-type-java
|
aieng-lab
| 2025-06-13T17:29:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-large",
"base_model:finetune:openai-community/gpt2-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-13T17:28:37Z |
---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-large
pipeline_tag: text-classification
---
# GPT-2 large for classifying code comments (multi-label)
This model classifies comments in Java code as 'summary', 'ownership', 'expand', 'usage', 'pointer', 'deprecation' or rational'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [gpt2-large](https://huggingface.co/gpt2-large)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
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