mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit-REAP-18B

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11.4 GB instead of 20.4 GB. 14.4 GB of memory to run.

	Parent	This model
On disk	20.4 GB	11.4 GB	−44%
Parameters	34.7B	18.3B	−47%

50% of the routed experts are removed from mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit; active parameters per token are unchanged, since top-8 routing is preserved and only the stored expert bank shrinks. That is why it gets smaller without getting slower.

Retained experts are copied bit-for-bit from the parent quant. Nothing is dequantized, re-quantized, merged, or retrained.

This variant was not separately benchmarked. It is published under the recipe validated end to end on Qwen3.6-35B-A3B-OptiQ-4bit-REAP-19B, the same architecture at the same 50 % retention: Capability Score 80.03 -> 76.57, with the loss concentrated in MMLU (-21.4) and procedural ability intact (GSM8K +2.6, IFEval +4.3, BFCL -1.0, HumanEval -1.3).

Two things were measured on this checkpoint. The ranking rule was chosen by scoring both candidates against the unpruned model, which picked the conditional mean. And the resulting divergence from the unpruned parent is KL 0.213 — for reference, the checkpoints that degrade visibly under pruning measure above 1.0, and this one is well inside the range where generation is indistinguishable in review.

Details

Property	Value
Experts retained	128 of 256 per layer
Active experts per token	8 (unchanged)
Allocation	uniform (128 of 256 in every layer)
Size	11.4 GB (parent 20.4 GB)
Parameters	18.3B (parent 34.7B)
Selection	REAP — mean of router weight x expert output norm, over the tokens each expert served
Calibration	optiq six-domain mix, 8 samples
MTP sidecar	absent

Use it

pip install mlx-optiq
optiq serve --model mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit-REAP-18B

from mlx_lm import load, generate
model, tok = load("mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit-REAP-18B")
print(generate(model, tok, prompt="Hello", max_tokens=64))

Method

Expert pruning follows REAP (Cerebras Research, ICLR 2026 — REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression). Experts are ranked by the conditional mean of router weight × expert output norm over calibration data; the lowest-ranked are removed and the router is sliced to match.

OptiQ applies it in the quantized domain — directly on a quantized checkpoint, with no BF16 parent and no dequantization of survivors — via optiq prune-experts. See the pruning docs.