Qwen3.6-35B-A3B pruned to 11B total / 3.05B active parameters, stored at 4-bit — 7.6× less expert memory than the 8-bit reference. Stock mlx_lm, no custom code.

Niwaki (庭木) are Japan's garden trees, sculpted by meticulous pruning so that every branch serves the form of the whole. This model applies that spirit to a Mixture-of-Experts: every routed expert is individually width-pruned using statistics of its routed tokens over a code-weighted mixed corpus (source from The Stack, code-reasoning traces, chat, and web), reconstructed to compensate, then distilled from the full model on the same mixed corpus, and stored at low precision. The result is an ordinary MoE — smaller, same architecture, standard fused kernels.

A paper with the full method and measurements is coming soon.

Benchmarks

Full evaluation protocol: WikiText-2 perplexity over 256 × 2048-token windows; task average over the complete arc_easy, hellaswag, piqa, winogrande, and boolq test sets, paired against the reference.

model	expert storage	wt2 ppl ↓	task avg ↑	retention	generation d2 (avg/min)
reference (8-bit)	34.2 GB	6.76	0.796	100%	0.89 / 0.77
this model	4.5 GB (0.132×)	16.88	0.664	83.5%	0.82 / 0.62

Generation quality is measured, not assumed: an 8-prompt battery (code, reasoning, chat, creative; 600-token sampled generations) scored by bigram diversity. Known limitation (measured): sustained code generation is the weakest axis of this model (battery minimum 0.62 on long-file code prompts, vs 0.75+ for its larger siblings); output tends to truncate early rather than stay coherent to the end of a long file. Chat, reasoning, and short-form writing are functional. Choose the 19B for heavy code work.

Model dimensions


total parameters	11B (from 35B)
active parameters per token	~3.05B (from ~3.8B: 2.8B backbone + 8 pruned experts)
layers / routed experts / top-k	40 / 256 / 8
expert intermediate size	128 (from 512)
expert precision	4-bit (group 64)
backbone precision	8-bit (untouched)
whole-model size on disk	~7.4 GB
runs comfortably in	12 GB unified memory

Usage (MLX, Apple Silicon)

python

from mlx_lm import load, generate

model, tokenizer = load("neopolita/Qwen3.6-11B-A3B-Niwaki-4bit-mlx")
print(generate(model, tokenizer, prompt="...", max_tokens=256))

GGUF builds for llama.cpp: Qwen3.6-11B-A3B-Niwaki-4bit-GGUF (UD-Q3K recommended + Q4_K_M).

Notes and limitations

Compression trades quality: this model sits at ~84% of the reference task average and 2.50× its perplexity. Choose the family member that fits your memory budget.
Evaluated text-only on English-web-heavy data; the base model's biases are inherited and rare-domain behaviour of pruned experts is less tested.
Part of the Niwaki family: 27B-A3B-Niwaki-2bit · 19B-A3B-Niwaki-2bit.

Base model by the Qwen team (Apache 2.0); 8-bit MLX conversion by mlx-community; pruning and distillation by the Niwaki project, 2026-08. Method details: paper coming soon.