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Model: opencsg/OpenCSG-R1-Qwen2.5-Code-3B-V1 Source: Original Platform
2026-06-03 08:58:13 +08:00
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+---
+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}}
+}
+```