Update README.md

README.md

@@ -1,22 +1,96 @@
 ---
+language:
+- en
+- zh
+license: apache-2.0
+base_model: Qwen/Qwen3.5-27B
 tags:
-- gguf
-- llama.cpp
 - unsloth
-- vision-language-model
+- qwen
+- qwen3.5
+- reasoning
+- chain-of-thought
+- Dense
+pipeline_tag: text-generation
+datasets:
+- Jackrong/Qwen3.5-reasoning-700x
+- nohurry/Opus-4.6-Reasoning-3000x-filtered
 ---
 
-# Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF : GGUF
+# 🌟 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+> 📢 **Release Note**
+>
+> To address potential runtime errors in inference frameworks with the early quantized version, the current weights have been fully rebuilt utilizing the latest toolchain. I have re-executed the fine-tuning process and GGUF quantization in an updated environment to ensure maximum compatibility and stability.
+>
+> **Build Environment Upgrades:**
+> - **Fine-tuning Framework**: **Unsloth 2026.3.3** (with the latest *Fast Qwen3_5 patching* applied)
+> - **Core Dependencies**: **Transformers 5.2.0**
+>
+>
+![HB8AleUaMAArNyM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/GHkMJL6I383eIwK1qj80K.jpeg)
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF --jinja`
-- For multimodal models: `llama-mtmd-cli -hf Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF --jinja`
 
-## Available Model files:
-- `Qwen3.5-27B.Q8_0.gguf`
-- `Qwen3.5-27B.BF16-mmproj.gguf`
-- `Qwen3.5-27B.BF16-00002-of-00002.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## 💡 Model Introduction
+**Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled** is a highly capable reasoning model fine-tuned on top of the powerful Qwen3.5 architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions. 
+
+Through Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted `<think>` tags, and ultimately delivering precise, nuanced solutions. 
+
+### 🧠 Example of Learned Reasoning Scaffold（Example）
+
+The model includes targeted optimizations addressing Qwen3.5’s tendency toward excessive transitional or repetitive reasoning on simple queries. Through deep distillation and structural imitation of Claude-4.6-Opus reasoning chains, the model adopts a more efficient structured thinking pattern:  
+**“Let me analyze this request carefully: 1..2..3...”.**  
+This streamlined reasoning paradigm significantly reduces redundant cognitive loops while preserving deep analytical capacity, resulting in substantially improved inference efficiency.
+
+```text
+Let me analyze this request carefully:
+
+1. Identify the core objective of the problem.
+2. Break the task into clearly defined subcomponents.
+3. Evaluate constraints and edge cases.
+4. Formulate a step-by-step solution plan.
+5. Execute the reasoning sequentially and verify consistency.
+            .
+            .
+            .
+```
+
+## 🗺️ Training Pipeline Overview
+
+```text
+Base Model (Qwen3.5-27B)
+ │
+ ▼
+Supervised Fine-Tuning (SFT) + LoRA
+ │
+ ▼
+Final Model (Claude-4.6-Opus-Reasoning-Distilled,text-only)
+```
+
+## 📋 Stage Details
+
+### 🔹 Supervised Fine-Tuning (SFT)
+- **Objective:** To inject high-density reasoning logic and establish a strict format for problem-solving involving an internal thinking state prior to outputting the final response.
+- **Methodology:** We utilized **Unsloth** for highly efficient memory and compute optimization (LoRA Rank = 64). A critical component of this stage is the `train_on_responses_only` strategy, masking instructions so the loss is purely calculated over the generation of the `<think>` sequences and the subsequent solutions. 
+- **Format Enforcement:** All training samples were systematically normalized so the model strictly abides by the structure `<think> {internal reasoning} </think>\n {final answer}`.
+
+### 📚 All Datasets Used
+The dataset consists of high-quality, filtered reasoning distillation data:
+
+| Dataset Name | Description / Purpose |
+|--------------|-----------------------|
+| [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. |
+| [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. |
+
+## 🌟 Core Skills & Capabilities
+1. **Modular & Structured Thinking:** Inheriting traits from Opus-level reasoning, the model demonstrates confident parsing of the prompt, establishing an outlined plan in its `<think>` block sequentially rather than exploratory "trial-and-error" self-doubt.
+2. **Extended Context Support:** Fine-tuned smoothly with an 8192 context window allowing complex multi-step reasoning traces to exist gracefully within memory limits.
+
+## ⚠️ Limitations & Intended Use
+- **Hallucination Risk:** While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.
+- **Intended Scenario:** Best suited for offline analytical tasks, coding, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic.
+- **Preview Version Notice:** Because this model is relatively new and intentionally lightweight, the surrounding ecosystem — including inference templates, fine-tuning pipelines, routing configurations, and tooling integrations — may not yet be fully mature or standardized. As a result, users may encounter occasional bugs, compatibility inconsistencies, or integration edge cases. The current release should be considered a preview build while the broader architectural stack and supporting utilities continue to stabilize and improve.
+
+## 🙏 Acknowledgements
+Significant thanks to the [Unsloth AI](https://unsloth.ai/) team for making rapid fine-tuning of MoE and large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets (`nohurry` and `TeichAI`).