| | --- |
| | 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}} |
| | } |
| | ``` |
