| --- |
| license: apache-2.0 |
| datasets: |
| - open-r1/OpenThoughts-114k-Code_decontaminated |
| base_model: |
| - Qwen/Qwen2.5-Coder-3B-Instruct |
| library_name: transformers |
| tags: |
| - code |
| - grpo |
| - open-r1 |
| --- |
| |
| # Model Card for OpenCSG-R1-Qwen2.5-Code-3B-V1 |
|
|
| This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-3B-Instruct] (https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) on the [open-r1/OpenThoughts-114k-Code_decontaminated] datasets. |
| It has been trained using [TRL](https://github.com/huggingface/trl). |
| |
| ## Quick start |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| import pandas as pd |
| |
| model_name = "/data/project/pj/r1/opencsg-r1/open-r1/train/Qwen2.5-3B-Open-R1-Code-GRPO/checkpoint-150" |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=False) |
| df = pd.read_parquet('/data/project/pj/r1/opencsg-r1/OpenThoughts-114k-Code_decontaminated/train-00000-of-00006.parquet') |
| data = df['problem'][0] |
| messages = [ |
| { |
| "role": "user", |
| "content": f"Please help me solve the problem: {data}.Output the thinking process within the <think> </think> tags,and then return the final result within the <answer> </answer> tags.", |
| }, |
| { |
| "role": "assistant", |
| "content": "Let's solve the problem step by step.\n<think>", |
| }, |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| continue_final_message=True, |
| # add_generation_prompt=True |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=1024, |
| temperature=0.6 |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| print(response) |
| ``` |
| |
| 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: 4.49.0 |
| - 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}} |
| } |
| ``` |
