Instructions to use Tesslate/Gradience-RL-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tesslate/Gradience-RL-Math with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tesslate/Gradience-RL-Math", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from Tesslate/Gradience-RL-Math: direct link, hf CLI and curl.
- Browser
- Download file 2.05 kB
-
https://huggingface.co/Tesslate/Gradience-RL-Math/resolve/main/README.md
- Command line
-
hf download hf://Tesslate/Gradience-RL-Math/README.md
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curl -L -o README.md https://huggingface.co/Tesslate/Gradience-RL-Math/resolve/main/README.md
2.05 kB
| base_model: smirki/Gradience-T1-3B-Merged-2000 | |
| library_name: transformers | |
| model_name: Gradience-RL-Math | |
| tags: | |
| - generated_from_trainer | |
| - unsloth | |
| - trl | |
| - grpo | |
| licence: license | |
| # Model Card for Gradience-RL-Math | |
| This model is a fine-tuned version of [smirki/Gradience-T1-3B-Merged-2000](https://huggingface.co/smirki/Gradience-T1-3B-Merged-2000). | |
| 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="Tesslate/Gradience-RL-Math", device="cuda") | |
| output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] | |
| print(output["generated_text"]) | |
| ``` | |
| ## Training procedure | |
| This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). | |
| ### Framework versions | |
| - TRL: 0.15.2 | |
| - Transformers: 4.50.0.dev0 | |
| - Pytorch: 2.5.1 | |
| - Datasets: 3.3.2 | |
| - Tokenizers: 0.21.0 | |
| ## Citations | |
| Cite GRPO as: | |
| ```bibtex | |
| @article{zhihong2024deepseekmath, | |
| title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, | |
| author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, | |
| year = 2024, | |
| eprint = {arXiv:2402.03300}, | |
| } | |
| ``` | |
| Cite TRL as: | |
| ```bibtex | |
| @misc{vonwerra2022trl, | |
| title = {{TRL: Transformer Reinforcement Learning}}, | |
| author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, | |
| year = 2020, | |
| journal = {GitHub repository}, | |
| publisher = {GitHub}, | |
| howpublished = {\url{https://github.com/huggingface/trl}} | |
| } | |
| ``` |