Qwen3.6-28B

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Qwen3.6-28B

REAP-pruned Qwen/Qwen3.6-35B-A3B.

At a glance

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Qwen3.6-28B-REAP20-Opus-A3B

A 20%-expert-pruned + Opus-trace fine-tuned variant of Qwen/Qwen3.6-35B-A3B, produced via Cerebras REAP (Router-weighted Expert Activation Pruning, arXiv:2510.13999) followed by LoRA SFT on public Claude Opus reasoning traces.

Headline numbers

Architecture

• Base: Qwen3.6-35B-A3B (40 layers, 256 experts/layer, 8 routed + 1 shared active, qwen3_5_moe)
• After 20% REAP: 205 experts/layer kept, 51 experts/layer pruned → ~28B total params, still ~3B active
• Fine-tune: LoRA rank 32, α 64 on q,k,v,o,gate,up,down projections. bf16 weights after merge.

Pipeline

1. Calibration merge — 5,000 stratified samples from:
   • /Users/sero/.../reap-expert-swap/dataset/calibration-20k.jsonl (general, coding, reasoning, etc.)
   • 0xSero/structured-outputs-calibration-v1 (JSON / Mermaid / schema)
2. REAP observation (this fork's Qwen3_5Moe-aware observer, multi-GPU layerwise on 8× A100-40GB): {{OBS_DURATION}}
3. REAP prune @ 20% using reap saliency metric, renormalized router weights, seed 42.
4. Opus-trace SFT via LLaMA-Factory + DeepSpeed ZeRO-3 (8× A100). LoRA 2 epochs on nohurry/Opus-4.6-Reasoning-3000x-filtered (2,326 reasoning trajectories with explicit <think>…</think>\nanswer structure).
5. GGUF — bf16, Q8_0, Q6_K, Q5_K_M, Q4_K_M with imatrix from merged calibration.

Sidecar observations

REAP observation artifacts live in the separate dataset repo 0xSero/qwen3.6-35b-a3b-reap-observations.

Known limitations

• Refusal behavior follows the base model plus Opus SFT; no explicit abliteration was applied in this release. The model will refuse straight adversarial probes at roughly base-model rates.
• Reasoning quality on GSM8K-style problems depends on the <think> chain-of-thought; short max-tokens limits hurt accuracy.
• Structured-output calibration is oversampled vs. base mix (JSON/Mermaid experts preferentially retained).

License

Apache 2.0, inherited from base model. This checkpoint is a derivative work; please preserve attribution.

License & citation

License inherited from the base model.

@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}

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