CDDRS

README_en.md

@@ -52,6 +52,25 @@
 | [Chapter 5: Building Large Models from Scratch](./docs/chapter5/第五章%20动手搭建大模型.md) | Implementing LLaMA2, training Tokenizer, pre-training small LLM | ✅ |
 | [Chapter 6: Large Model Training Practice](./docs/chapter6/第六章%20大模型训练流程实践.md) | Pre-training, supervised fine-tuning, LoRA/QLoRA efficient fine-tuning | 🚧 |
 | [Chapter 7: Large Model Applications](./docs/chapter7/第七章%20大模型应用.md) | Model evaluation, RAG retrieval enhancement, Agent intelligent agents | ✅ |
+| [Extra Chapter LLM Blog](./Extra-Chapter/) | Excellent Learning Notes/Blog on LLMs ，Welcome PR ！| 🚧 |
+
+### Extra Chapter LLM Blog
+
+- [With large models becoming so powerful, what’s the significance of fine-tuning a 0.6B small model?](./Extra-Chapter/why-fine-tune-small-large-language-models/readme.md) @[不要葱姜蒜](https://github.com/KMnO4-zx) 2025-7-11
+
+- [Details of the Transformer modules](./Extra-Chapter/transformer-architecture/) @[ditingdapeng](https://github.com/ditingdapeng) 2025-7-14
+
+- [Detailed Explanation of Text Data Processing](./Extra-Chapter/text-data-processing/readme.md) @[蔡鋆捷](https://github.com/xinala-781) 2025-7-14
+
+- [Qwen3-"VL"——Path to 'Concatenation Fine-tuning' for Ultra-small Chinese Multimodal Models](./Extra-Chapter/vlm-concatenation-finetune/README.md) @[ShaohonChen](https://github.com/ShaohonChen) 2025-7-30
+
+- [S1: Thinking Budget with vLLM](./Extra-Chapter/s1-vllm-thinking-budget/readme.md) @[kmno4-zx](https://github.com/kmno4-zx) 2025-8-03
+
+- [CDDRS: Key elements guided Enhancement for RAG-based Retrieval Methods](./Extra-Chapter/CDDRS/readme.md) @[Hongru0306](https://github.com/Hongru0306) 2025-8-21
+
+
+>   * If anyone has unique insights, knowledge, or practices related to the Happy-LLM project or LLMs in general, you are welcome to submit a PR to the [Extra Chapter LLM Blog](./Extra-Chapter/). Please adhere to the [PR Guidances](./Extra-Chapter/Readme.md). We will decide whether to merge or supplement the content into the main Happy-LLM text based on the quality and value of the PR.*
+
 
 ### Model Downloads