Update README.md

README.md

@@ -46,7 +46,8 @@ Using Magpie, three-round dialogue data was synthesized for tasks including:
 - **Advice-seeking**: Offers thoughtful advice and guidance, helping users address personal, professional, or life challenges.
 - **Brainstorming**: Generates ideas and fosters creative thinking, assisting users in exploring possibilities and proposing innovative concepts.
 
-#### **2. Additional Tasks Referenced from SmolTalk**
+#### 
+**2. Additional Tasks Referenced from SmolTalk**
 
 Using Magpie, one-round dialogue tasks were synthesized for:
 
@@ -81,7 +82,7 @@ The construction of the **smoltalk-chinese** dataset adheres to strict standards
 
 #### **Deduplication**
 
-- The **gte-large-zh** model encoded the first instruction in the dialogue data. Deduplication was performed based on embedding similarity (threshold set at 0.8), ensuring uniqueness and diversity in the data.
+- The **gte-large-zh** model encoded the first instruction in the conversation data. Deduplication was performed based on embedding similarity (threshold set at 0.8), ensuring the diversity of the data.
 
 #### **Task Type and Text Length Statistics**
 
@@ -99,7 +100,7 @@ To verify the fine-tuning effectiveness of the **smoltalk-chinese** dataset, the
    The base model used was **opencsg/csg-wukong-ablation-chinese-fineweb-edu** (a 2B model pretrained on the **chinese-fineweb-edu** dataset).
 
 2. **Fine-tuning Process**
-   Fine-tuning was performed on both the **smoltalk-chinese** and **infinity-instruct** datasets (selecting 7M entries and the Chinese subset of approximately 1M entries), with the following settings:
+   Fine-tuning was performed on **smoltalk-chinese**, **Magpie-Qwen2-Pro-200K-Chinese** and **infinity-instruct** datasets (selecting 7M entries and the Chinese subset of approximately 1M entries), with the following settings:
 
    - **Epochs**: 2
    - **Learning Rate**: 3e-4
@@ -107,7 +108,7 @@ To verify the fine-tuning effectiveness of the **smoltalk-chinese** dataset, the
    - **Global Batch Size**: 32
 
 3. **Evaluation Results**
-   The model's Chinese conversational capabilities were evaluated on the **alignbench** platform. Results demonstrated significant advantages for the model fine-tuned on the **smoltalk-chinese** dataset across multiple metrics, confirming the dataset's effectiveness in improving Chinese language model performance.
+   The model's Chinese conversational capabilities were evaluated on [**Alignbench**](https://github.com/THUDM/AlignBench). Results demonstrated significant advantages for the model fine-tuned on the **smoltalk-chinese** dataset across multiple metrics, confirming the dataset's effectiveness in improving Chinese language model performance.
 
    | Dataset                       | Professional Skills | Chinese Comprehension | Basic Tasks | Math Calculation | Text Writing | General Q&A | Role Playing | Logical Reasoning | Chinese Reasoning | Chinese Language | Total Score |
    | ----------------------------- | ------------------- | --------------------- | ----------- | ---------------- | ------------ | ----------- | ------------ | ----------------- | ----------------- | ---------------- | ----------- |
@@ -228,14 +229,14 @@ smoltalk-chinese 数据集的构建过程严格遵循高标准，确保数据的
 
 ### **微调过程**
 
-分别在 smoltalk-chinese 和 infinity-instruct 数据集（选取7M和Gen的中文部分，约1M条）上进行微调，设置为
+分别在 smoltalk-chinese 和 Magpie-Qwen2-Pro-200K-Chinese 和 infinity-instruct 数据集（选取7M和Gen的中文部分，约1M条）上进行微调，训练设置为
 
 - **Epochs**: 2
 - **Learning Rate**: 3e-4
 - **Scheduler**: Cosine decay
 - **Global Batch Size**: 32
 
-在 alignbench 平台上评估模型的中文对话能力，结果表明，基于 smoltalk-chinese 微调的模型在多个指标上表现出显著优势，验证了 smoltalk-chinese 数据集在提升中文语言模型表现方面的有效性。
+在 [**Alignbench**](https://github.com/THUDM/AlignBench) 上评估模型的中文对话能力，结果表明，基于 smoltalk-chinese 微调的模型在多个指标上表现出显著优势，验证了 smoltalk-chinese 数据集在提升中文语言模型表现方面的有效性。
 
 | 数据集                        | 专业能力 | 中文理解 | 基本任务 | 数学计算 | 文本写作 | 综合问答 | 角色扮演 | 逻辑推理 | 中文推理 | 中文语言 | 总分 |
 | ----------------------------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | ---- |