在数据生成后，您可以使用[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 和 [xtuner](https://github.com/InternLM/xtuner)对大语言模型进行微调。
以下是在超过 50 % 的 SFT 数据来自 GraphGen 及我们的数据清洗流程时的训练后结果：
| 领域 | 数据集 | 我们的方案 | Qwen2.5-7B-Instruct（基线） |
| :-: | :-------------------------------------------------------: | :------: | :---------------------: |
| 植物 | [SeedBench](https://github.com/open-sciencelab/SeedBench) | **65.9** | 51.5 |
| 常识 | CMMLU | 73.6 | **75.8** |
| 知识 | GPQA-Diamond | **40.0** | 33.3 |
| 数学 | AIME24 | **20.6** | 16.7 |
| | AIME25 | **22.7** | 7.2 |
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