Jackrong
/

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2

@@ -19,20 +19,19 @@ datasets:
 - Roman1111111/claude-opus-4.6-10000x
 ---
 # 🌟 Qwen3.5-27B-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.**
-> ❤️ Special thanks to the Unsloth open-source library and @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)**
 ### 📥 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.
@@ -41,6 +40,8 @@ You can click the link below to directly access the complete technical manual fo
 > 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.
 >
 > *No one starts as an expert. But every expert was once brave enough to begin.*
 ---

 - Roman1111111/claude-opus-4.6-10000x
 ---
 # 🌟 Qwen3.5-27B-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.**
+> ❤️ 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)**
 ### 📥 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.
 > 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.
 >
 > *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! 🙏
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