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Cataloged from Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
All models by Jackrong →🔥 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 open-source library and @KyleHessling1 for their support.
👉 GitHub Repository: Jackrong-llm-finetuning-guide Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)
A Note: 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.
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.
Build Environment Upgrades:
- Fine-tuning Framework: Unsloth 2026.3.3
- Core Dependencies: Transformers 5.2.0
- This model fixes the crash in the official model caused by the Jinja template not supporting the "developer" role. (commonly sent by modern coding agents like Claude Code and OpenCode)
- It does not disable thinking mode by default, and allowing the agent to run continuously for over 9 minutes without interruption.
- Compared to the original model, autonomy and stability are significantly improved.
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.
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.
🔧Tool Calling Benchmark（benchmark tests by user @Chris Klaus）
From the test results, it is clear that different Qwen3.5 quantized models show significant differences in tool-calling capability. Among them, only the 27B model distilled with Claude Opus reasoning demonstrates stable performance.
🔥Community-tested advantages (benchmark tests by user @sudoing on a single RTX 3090):
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled shows significant advantages in coding-agent environments such as Claude Code and OpenCode:
- Native support for the “developer” role, requiring no Jinja template patches or ChatML workarounds.
- Thinking mode fully preserved (logs confirm
thinking=1), not silently disabled, maintaining the complete chain-of-thought reasoning process.- Greatly improved autonomy and stability — capable of running continuously for over 9 minutes autonomously (with zero human intervention). It actively waits for tool responses, reads outputs, self-corrects errors, and can even automatically generate a README, whereas the base model often stalls or freezes mid-execution.
Hardware usage remains unchanged:
- About 16.5 GB VRAM with Q4_K_M quantization
- 29–35 tok/s generation speed
- Full 262K context with no compromises
train_on_responses_only strategy, masking instructions so the loss is purely calculated over the generation of the <think> sequences and the subsequent solutions.<think> {internal reasoning} </think>\n {final answer}.The dataset consists of high-quality, filtered reasoning distillation data:
<think> block sequentially rather than exploratory "trial-and-error" self-doubt.Significant thanks to the 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).
If you use this model in your research or projects, please cite:
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. . . .Base Model (Qwen3.5-27B) │ ▼Supervised Fine-Tuning (SFT) + LoRA │ ▼Final Model (Claude-4.6-Opus-Reasoning-Distilled,text-only)@misc{jackrong_qwen35_opus_distilled, title = {Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled}, author = {Jackrong}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled}}}