| --- |
| base_model: Qwen/Qwen2.5-1.5B-Instruct |
| library_name: transformers |
| model_name: MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b |
| tags: |
| - generated_from_trainer |
| - trl |
| - grpo |
| - deepseek |
| - r1 |
| licence: license |
| license: apache-2.0 |
| datasets: |
| - bhaviktheslider/JSON-Unstructured-Structured |
| --- |
| |
| # Model Card for MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b |
|
|
| This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). |
| It has been trained using [TRL](https://github.com/huggingface/trl). |
|
|
|
|
| Datasets: |
| - MasterControlAIML/JSON-Unstructured-Structured |
| |
| --- |
|
|
| # DeepSeek R1 Strategy Replication on Qwen-2.5-1.5b on 8*H100 GPUS |
| |
| *Problem - Unstructured to Structured JSON Creation* |
|
|
|
|
| *Desired Input - Unstructured Text Paragraphs and Blank Schema Rules* |
|
|
| *Output - Filled Created JSON from Unstructured Text following Blank Schema Rules* |
|
|
| *Dataset Link to Understand More - https://huggingface.co/datasets/MasterControlAIML/JSON-Unstructured-Structured* |
|
|
|
|
| ## 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="MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b", 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/bhavik18385-mastercontrol/grpo_training/runs/uyerl4vn) |
|
|
|
|
| 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: 0.14.0 |
| - Transformers: 4.48.1 |
| - Pytorch: 2.5.1+cu121 |
| - Datasets: 3.1.0 |
| - 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}} |
| } |
| ``` |
