初始化项目，由ModelHub XC社区提供模型

Model: open-r1/OpenR1-Qwen-7B
Source: Original Platform

README.md

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+---
+datasets: open-r1/openr1-220k-math
+library_name: transformers
+model_name: OpenR1-Qwen-7B
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+license: apache-2.0
+---
+
+# OpenR1-Qwen-7B
+
+This is a finetune of [Qwen2.5-Math-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on [OpenR1-220k-Math](https://huggingface.co/datasets/open-r1/openr1-220k-math) (`default` split).
+
+> [!NOTE]
+> Check out [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B) for an improved model that was trained on [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts) and replicates the performance of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) across multiple reasoning domains.
+
+## Quick start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "open-r1/OpenR1-Qwen-7B"
+device = "cuda" 
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
+
+messages = [
+    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
+    {"role": "user", "content": prompt}
+]
+```
+
+## Training 
+
+We train the model on the `default` split of [OpenR1-220k-Math](https://huggingface.co/datasets/open-r1/openr1-220k-math) for 3 epochs. We use learning rate of 5e-5 and extend the context length from 4k to 32k, by increasing RoPE frequency to 300k. The training follows a linear learning rate schedule with a 10% warmup phase. The table below compares the performance of OpenR1-Qwen-7B to [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) and [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B) using [lighteval](https://github.com/huggingface/open-r1/tree/main?tab=readme-ov-file#evaluating-models).
+
+You can find the training and evaluation code at: https://github.com/huggingface/open-r1/
+
+| Model                    | MATH-500 | AIME 2024 | AIME 2025 | GPQA-D |
+|--------------------------|----------|-----------|-----------|--------|
+| DeepSeek-Distill-Qwen-7B | 93.5     | 51.3      | 35.8      | 52.4   |
+| OpenR1-Qwen-7B           | 90.6     | 47.0      | 33.2      | 42.4   |
+| OpenThinker-7B           | 86.4     | 31.3      | 24.6      | 39.1   |