| ---
|
| base_model: Qwen/Qwen2.5-1.5B-Instruct
|
| library_name: transformers
|
| tags:
|
| - generated_from_trainer
|
| - trl
|
| - grpo
|
| - deepseek
|
| - r1
|
| licence: license
|
| license: apache-2.0
|
| datasets:
|
| - bhaviktheslider/JSON-Unstructured-Structured
|
| language:
|
| - zho
|
| - eng
|
| - fra
|
| - spa
|
| - por
|
| - deu
|
| - ita
|
| - rus
|
| - jpn
|
| - kor
|
| - vie
|
| - tha
|
| - ara
|
| ---
|
|
|
| # 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}}
|
| }
|
| ``` |
