docs: update README

Author: ChenZiHong-Gavin

README.md

@@ -56,6 +56,8 @@ Here is post-training result which **over 50% SFT data** comes from GraphGen and
 It begins by constructing a fine-grained knowledge graph from the source text，then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge.
 Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data.
 
+After data generation, you can use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) and [xtuner](https://github.com/InternLM/xtuner) to finetune your LLMs.
+
 ## 📌 Latest Updates
 
 - **2025.08.14**: We have added support for community detection in knowledge graphs using the Leiden algorithm, enabling the synthesis of Chain-of-Thought (CoT) data.

README_ZH.md

@@ -57,6 +57,8 @@ GraphGen 是一个基于知识图谱引导的合成数据生成框架。请查
 GraphGen 首先根据源文本构建细粒度的知识图谱，然后利用期望校准误差指标识别大语言模型中的知识缺口，优先生成针对高价值长尾知识的问答对。  
 此外，GraphGen 采用多跳邻域采样捕获复杂关系信息，并使用风格控制生成来丰富问答数据的多样性。
 
+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。
+
 ## 📌 最新更新
 
 - **2025.08.14**：支持利用 Leiden 社区发现算法对知识图谱进行社区划分，合成 CoT 数据。