Unsloth Model Card

README.md

@@ -1,83 +1,21 @@
 ---
-language:
-- en
-- zh
-license: lgpl-3.0
-base_model: Qwen/Qwen3.5-27B
+base_model: qwen/Qwen3.5-27B
 tags:
+- text-generation-inference
+- transformers
 - unsloth
-- qwen
-- qwen3.5
-- reasoning
-- chain-of-thought
-- Dense
-pipeline_tag: text-generation
+- qwen3_5
+license: apache-2.0
+language:
+- en
 ---
 
-# 🌟 Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
-
-
-![HB8AleUaMAArNyM](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/GHkMJL6I383eIwK1qj80K.jpeg)
-
-## 💡 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.|
+# Uploaded finetuned  model
 
-## 🌟 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.
+- **Developed by:** Jackrong
+- **License:** apache-2.0
+- **Finetuned from model :** qwen/Qwen3.5-27B
 
-## ⚠️ 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.
+This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-## 🙏 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`).
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)