Timersofc/Qwen3.5-Creative-26B-A3B-REAP

Qwen3.5-Creative-26B-A3B

A creative-writing-optimized pruning of Qwen/Qwen3.5-35B-A3B using REAP.

25% of MoE experts pruned (256 → 192) using a creative writing calibration dataset. A lighter prune that preserves more reasoning capability while still significantly reducing model size.

What is this?

	Base Model	This Model	50% Prune
Total params	~35B	~26B	~18B
Active params/token	~3B	~3B	~3B
MoE experts	256	192	128
Q4_K_M GGUF	~21GB	~15GB	~10GB
Target VRAM	24GB+	24GB	16-24GB

How it was made

1. Calibration dataset: 3000 samples — 1000 each from WritingPrompts, Project Gutenberg, and Roleplay scenarios (Timersofc/creative-writing-reap-calibration)
2. REAP profiling: Router-weighted expert activation norms recorded across all 40 MoE layers
3. Pruning: Bottom 25% of experts by REAP score removed globally

Why this over the 50% prune?

The 50% version (Timersofc/Qwen3.5-Creative-18B-A3B) is smaller and faster but chain-of-thought reasoning is less stable. This 25% version retains more of the original model's reasoning experts, making it better for:

• Long-form storytelling requiring plot consistency
• Complex character work needing CoT planning
• Any task where you want reliable thinking mode

If you just need short-form creative output and want maximum compression, the 50% version is better value.

Usage notes

• Works with standard Qwen3.5 chat templates
• For thinking mode: model should produce <think>...</think> blocks naturally
• Prefilling with <think>\nOkay, can help ensure CoT engagement

GGUF quantizations

Available in Timersofc/Qwen3.5-Creative-26B-A3B-GGUF:

• Q4_K_M (imatrix) — ~15GB, recommended for 24GB VRAM
• Q6_K (imatrix) — ~19GB, higher quality
• f16 — full precision GGUF for custom quantization

All quantizations use an importance matrix generated from the same creative writing calibration dataset used for REAP profiling. This means bit allocation within each tensor is optimized for creative writing — weights that matter most for prose quality get higher precision.

Credits

• Qwen team for the base model
• Cerebras Research for the REAP method
• REAP fork with Qwen3.5 patches: janmts/reap

License

Same as the base model. This is an unofficial community variant, not affiliated with Alibaba or Cerebras.