Update README.md

README.md

@@ -17,33 +17,33 @@ datasets:
 - nohurry/Opus-4.6-Reasoning-3000x-filtered
 - Jackrong/Qwen3.5-reasoning-700x
 - Roman1111111/claude-opus-4.6-10000x
-- TeichAI/claude-4.5-opus-high-reasoning-250x
 ---
 
 # 🌟 Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
-🔥 **Update (April 5): To help beginners and enthusiasts better understand and reproduce the fine-tuning process of this model, I have prepared the complete training notebook, codebase, and a comprehensive companion PDF guide! Please check the resource links below.**
+🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 
 
 > ❤️ Special thanks to the [**Unsloth**](https://unsloth.ai) open-source library and [@KyleHessling1](https://x.com/kylehessling1) for their support.
 
 ## 📚 Resources & Guides
 
-If you want to dive into how this model was trained, or wish to reproduce the results locally or on Colab, please visit my GitHub repository:
-👉 **🔗[Jackrong-llm-finetuning-guide](https://github.com/R6410418/Jackrong-llm-finetuning-guide.git)**
+👉 **[GitHub Repository: Jackrong-llm-finetuning-guide](https://github.com/R6410418/Jackrong-llm-finetuning-guide.git)**
+Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
 
-### 📥 Core Technical Document Direct Download
-You can click the link below to directly access the complete technical manual for the Qwopus3.5 training:
-
-* **🔗[Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf](https://github.com/R6410418/Jackrong-llm-finetuning-guide/blob/8eb33234856054d23675064177de1ac10b54a609/guidePDF/Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf)**
-  * Covers the entire workflow, starting with an introduction to Google Colab and Unsloth.
-  * Details the complete pipeline with step-by-step explanations—from downloading the base model and normalizing heterogeneous data sources into a unified format, to configuring trainer hyperparameters and finally publishing to Hugging Face.
-  * Feedback is highly welcome! If you spot any shortcomings or areas for improvement, please let me know, and I will update it promptly.
+### 📥 Core Technical Document
+**🔗 [Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)](https://github.com/R6410418/Jackrong-llm-finetuning-guide/blob/main/guidePDF/Qwopus3-5-27b-Colab_complete_guide_to_llm_finetuning.pdf)**
+* **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face.
+* **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth.
+* *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
 
 > **A Note:**
-> My goal in writing this guide goes beyond merely detailing a single training workflow. I want to convey a broader message: fine-tuning, post-training, and even medium-scale pre-training are not unattainable technical rituals, nor are they the exaggerated hype often packaged by social media. More often than not, all you need is a Google account, a standard laptop, and relentless curiosity. 
+> My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. 
 > 
-> *No one starts as an expert. But every expert was once brave enough to begin.*
+> *No one starts as an expert, but every expert was once brave enough to begin.*
 > 
-> All fine-tuning training and testing for this project were conducted at my own expense. If you find this model or the guide helpful, a **Star ⭐️ on GitHub** would be the greatest encouragement for me. Thank you so much! 🙏
+> All training and testing for this project were self-funded. If you find this model or guide helpful, a **Star ⭐️ on GitHub** would be the greatest encouragement. Thank you! 🙏
+
+> [!Note]
+> The Claude series model optimizations are named under the **Qwopus3.5 series**, with the latest version being **🌟Qwopus3.5-v3**.
 
 ---
 
@@ -134,7 +134,6 @@ The dataset consists of high-quality, filtered reasoning distillation data:
 |--------------|-----------------------|
 | [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | Provides comprehensive Claude 4.6 Opus reasoning trajectories. |
 | [Roman1111111/claude-opus-4.6-10000x](https://huggingface.co/datasets/Roman1111111/claude-opus-4.6-10000x) | Large-scale public Claude 4.6 Opus distillation data used to strengthen general reasoning transfer in v2. |
-| [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) | Injecting high-intensity, structured reasoning instances. |
 | [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) | Additional curated reasoning samples designed to strengthen structured step-by-step problem solving and improve reasoning diversity. |