hesamation's Qwen3.6 Distills Claude Opus Reasoning Into Open Weights for Free
A community fine-tune distills Claude Opus 4.6's chain-of-thought reasoning into Qwen3.6's efficient MoE architecture, hitting 110K downloads under Apache 2.0
- What it is: A community fine-tune that distills Claude Opus 4.6 chain-of-thought reasoning into Qwen3.6-35B-A3B, available as GGUF quants on Hugging Face.
- Performance: Self-reported MMLU-Pro jump from 42.86% to 75.71% (+32.85pp) on 70 questions — a smoke check, not a full benchmark.
- Architecture: Qwen3.6-35B-A3B is a sparse MoE model with 35B total parameters but only ~3B active per token, giving frontier capacity at 3B inference cost.
- Training: LoRA SFT on ~14,000 Claude Opus 4.6 reasoning traces across three datasets, trained with Unsloth, quantized with llama.cpp.
- Quants available: Q4_K_M (21.2 GB), Q5_K_M (24.7 GB), Q6_K (28.5 GB), Q8_0 (36.9 GB) — runs on 24GB Apple Silicon at Q4.
- License: Apache 2.0, free for commercial use; text-only (vision encoder not fine-tuned).
What happens when you take one of the most efficient open-weight architectures available and teach it to think like a frontier proprietary model? That's the experiment at the center of Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF, a community fine-tune by developer hesamation that has quietly racked up over 110,000 downloads and 273 likes on Hugging Face.
Two models walk into a fine-tune
This is a GGUF-quantized release of a reasoning SFT fine-tune of Qwen3.6-35B-A3B on Claude Opus 4.6-style chain-of-thought distillation data. The pitch is simple: Qwen3.6 is already a coding powerhouse, but its reasoning traces can be verbose and repetitive. Claude Opus 4.6 is one of the best structured thinkers available, but it lives behind a proprietary API. This fine-tune attempts to port the latter's reasoning style into the former's open weights.
Qwen3.6-35B-A3B uses a sparse Mixture-of-Experts (MoE) architecture, where only a small subset of the model's expert neurons fire for each input token. The model has 35 billion total parameters sitting in its weights, but the MoE router only activates a fraction of them for each forward pass , giving you the representational capacity of a 35B model at the compute cost of a 3B model. That efficiency is the whole reason this fine-tune is interesting: you're getting structured frontier-style reasoning at a price point that fits on consumer hardware.
How it was built
Developer hesamation fine-tuned Alibaba's Qwen3.6-35B-A3B on roughly 14,000 chain-of-thought reasoning traces distilled from Claude Opus 4.6. The training data was pulled from three sources:
- The
nohurry/Opus-4.6-Reasoning-3000x-filtereddataset (~3,900 samples) of Claude Opus reasoning trajectories - The
Jackrong/Qwen3.5-reasoning-700xdataset (700 curated Qwen reasoning samples) - The
Roman1111111/claude-opus-4.6-10000x
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