Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

A reasoning-distilled variant of Qwen3.6-35B-A3B taught to imitate the chain-of-thought style of Claude Opus 4.7, the frontier reasoning model from Anthropic. The goal: port Claude-grade reasoning behavior into a permissively-licensed Mixture-of-Experts model that an individual can actually run.

Why this model

• Claude-style reasoning, open weights. Claude Opus 4.7 is one of the strongest reasoning models available, but only via a proprietary API. This model has been fine-tuned on ~8k high-quality reasoning traces produced by Opus 4.7, teaching the base to think before answering — with explicit <think>…</think> blocks — in Claude's structure and cadence.
• Sparse activation, dense knowledge. The base is a 35B-parameter MoE with 256 experts, 8 routed + 1 shared, of which only about 3B parameters are active per token. You get the capacity of a 35B model at the inference cost of a small dense model. Full-quality bf16 inference runs on a single 80GB A100 or H100.
• Long thinking supported. 64k token context. The model routinely emits 5–30k tokens of <think> reasoning on hard problems before giving the final answer — which is the whole point of reasoning models, and why this one was specifically trained end-to-end with an upstream teacher that also reasons explicitly.
• Clean base to build on. LoRA adapter is also published separately (…-adapter), so you can apply the distillation to other checkpoints of the same base, or stack further fine-tunes.

Intended use

Built for hard reasoning: graduate-level STEM, competition math (AIME / MATH), code reasoning with explicit walk-through, multi-step logic puzzles, and agentic planning where explicit <think> helps correctness.

For short-turn conversational latency-sensitive workloads the thinking budget can be large; cap max_new_tokens or post-process to strip <think>…</think> blocks if you only want final answers in production.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)

messages = [{"role": "user", "content": "How many positive integers less than 1000 have digits that sum to 20?"}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=32768, do_sample=False)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Recommended backend: vLLM for serving — the MoE routing + KV cache benefit significantly from continuous batching.

vllm serve lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled \
  --dtype bfloat16 --max-model-len 65536 --gpu-memory-utilization 0.9

GGUF (LM Studio / llama.cpp)

Quantized GGUF weights are available for llama.cpp and LM Studio:

• IQ4_XS (18.9 GB) — fits in ~24 GB RAM/VRAM, default pick for LM Studio

Search lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled inside LM Studio's model browser once HF has indexed the GGUF repo (usually within an hour of publication). More quant levels (Q4_K_M, Q5_K_M, Q8_0) can be added on request.

Training

Base model	Qwen/Qwen3.6-35B-A3B (loaded via unsloth/Qwen3.6-35B-A3B for faster finetuning)
Teacher	Claude Opus 4.7 (Anthropic)
Training dataset	lordx64/reasoning-distill-opus-4-7-max-sft — reasoning traces from Claude Opus 4.7 reformatted into SFT conversations
Source dataset	lordx64/reasoning-distill-claude-opus-4-7-max — raw teacher traces (pre-SFT formatting)
Dataset size	~7,800 full conversations, assistant side trained including <think>…</think>
Method	SFT with Unsloth + TRL SFTTrainer + train_on_responses_only (loss only on assistant tokens)
LoRA config	r=16, alpha=16, dropout=0.0, targets=["q_proj","k_proj","v_proj","o_proj"] (attention-only)
Hyperparameters	lr=2e-5, cosine schedule, warmup_ratio=0.03, weight_decay=0.01, optimizer adamw_8bit
Batch	per_device=1, grad_accum=16, effective=16, 2 epochs = 978 steps
Sequence	4096 tokens during training (64k usable at inference — base supports it natively)
Precision	bf16 on 1× H200 141GB (HF Inference Endpoint, custom container)
Trainable	3.44M params out of 35.1B (0.01%)

Why attention-only LoRA on a MoE

The initial plan was full LoRA including the MoE expert FFNs (gate_proj/up_proj/down_proj). In the course of this project I filed and upstreamed a shape-mismatch fix to unsloth-zoo's MoE+LoRA grouped-mm path — unslothai/unsloth-zoo#601 — without which the expert-LoRA forward crashes on Qwen3.6's 256-expert layout. Even with that fix, single-GPU memory made expert-LoRA impractical for this run. Attention-only captures most of the signal on style distillation anyway (the point of this model) while leaving the expert FFNs' learned knowledge intact — a v2 training run with expert LoRA on multi-GPU is a natural next step if the style-only signal isn't enough.

Evaluation

Evaluated via lm-evaluation-harness (v0.4.9) with vLLM backend at 64k context, bf16. Custom eval path strips <think>…</think> from generations before the filter pipeline, uses per-task conventional fewshot counts, and runs with fewshot_as_multiturn=True so few-shot examples are proper chat turns rather than concatenated prompt text. Raw results JSON is public: lordx64/qwen3-6-distill-evals.

Benchmark	Setup	Score
GSM8K CoT	8-shot multiturn, limit 300	84.3% (flexible-extract) / 76.7% (strict-match)
MMLU-Pro	5-shot multiturn, limit 500	74.9%
AIME 2024	0-shot, full (30)	extraction fix in progress — model generates answers but not in a format the AIME extractor recognizes (\boxed{} vs plain prose)
AIME 2025	0-shot, full (30)	same — pending
GPQA Diamond	0-shot CoT, full (198)	same — pending
MATH-500	0-shot, limit 100	rerun pending (missing sympy / math_verify dep in the first run)

MMLU-Pro subject breakdown

Standard reasoning-model profile: strong on STEM, weaker on law/engineering. All subjects evaluated at limit 500, 5-shot multiturn.

Subject	Acc	Subject	Acc
Biology	86.0%	Chemistry	78.8%
Psychology	83.4%	Health	73.8%
Math	83.6%	Business	74.4%
Economics	83.0%	Other	72.6%
Physics	81.0%	Philosophy	71.3%
Computer Science	79.0%	History	70.9%
		Engineering	54.8%
		Law	55.6%

Full per-task JSON with stderr, filter configs, and timings lives in the evals dataset. The remaining tasks will be added to this table after a diagnostic rerun identifies why AIME/GPQA extraction is returning no-match on generated outputs.

Limitations

• Reasoning ≠ knowledge. Distillation transfers how to reason, not new facts. Anything the base Qwen3.6-35B-A3B doesn't already know, this model still doesn't know.
• Attention-only LoRA. Expert FFNs are untouched from the base — domains where Claude and Qwen3.6 diverge in factual priors may see uneven improvement.
• Long generations. The model will genuinely use tens of thousands of tokens on hard problems. Budget your max_new_tokens accordingly, and provide max_model_len ≥ 32k at inference.
• Distillation provenance. Training data was generated with Anthropic's Claude Opus 4.7 via API. Downstream users should confirm compliance with Anthropic's usage policies for their specific use case.

Citation

If you use this model, please cite the base and the distillation:

@misc{qwen36_a3b_2026,
  title  = {Qwen3.6-35B-A3B},
  author = {Qwen Team},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/Qwen/Qwen3.6-35B-A3B}},
}

@misc{lordx64_qwen36_distill_2026,
  title  = {Qwen3.6-35B-A3B distilled from Claude Opus 4.7 reasoning},
  author = {lordx64},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled}},
}

Acknowledgements

• Unsloth — 2× faster training of large MoE LoRA; the bug we hit and fixed was in their unsloth-zoo patches (credit for rapid review of PR #601).
• Anthropic — for the teacher model.
• Qwen team — for releasing Qwen3.6 with a permissive Apache-2.0 license, enabling work like this.
• lm-evaluation-harness (EleutherAI) — evaluation methodology.