| | --- |
| | base_model: Qwen/Qwen2.5-1.5B-Instruct |
| | library_name: transformers |
| | model_name: null |
| | tags: |
| | - generated_from_trainer |
| | - trl |
| | - grpo |
| | - deepseek |
| | - r1 |
| | licence: license |
| | license: apache-2.0 |
| | datasets: |
| | - bhaviktheslider/JSON-Unstructured-Structured |
| | --- |
| | |
| | # Model Card for DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured |
| |
|
| | This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). |
| | It has been trained using [TRL](https://github.com/huggingface/trl). |
| |
|
| | ## Quick start |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" |
| | generator = pipeline("text-generation", model="MasterControlAIML/DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured", 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/cnqeubat) |
| |
|
| |
|
| | 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 |
| | - Datasets: 3.1.0 |
| | - Tokenizers: 0.21.0 |
| |
|
| | --- |
| | license: apache-2.0 |
| |
|
| | 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* |
| | |
| | ## Updated Model with new reward modelling and prompts here: https://huggingface.co/MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured |
| | |
| | |
| | ## 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}} |
| | } |
| | ``` |
