This is a Korean-oriented, expert-pruned MLX release of mlx-community/Qwen3.6-35B-A3B-8bit. It applies REAP-style router-weighted expert activation pruning to the routed MoE experts while keeping the upstream 8-bit MLX format, top-8 routing, shared experts, tokenizer, chat template, and non-MoE components intact.

In this release, K-Guard is the name of the Korean-centered calibration profile used to collect routing and activation statistics before pruning. There is no fine-tuning or weight retraining.

Model summary

Property	Value
Base checkpoint	`mlx-community/Qwen3.6-35B-A3B-8bit`
Architecture	Qwen3.6 MoE (`qwen3_5_moe`)
MoE layers	40
Routed experts per layer	192, reduced from 256
Experts removed per layer	64 / 256 (25%)
Active routed experts per token	8, unchanged
Shared expert	Preserved
Quantization	MLX affine 8-bit, group size 64
Local checkpoint size	approximately 27.18 GiB
Training after pruning	None

Calibration and expert selection

Expert importance was observed on a deterministic Korean-centered mixture of 24,576 sequences:

8,192 general Korean instruction and reasoning records
8,192 Korean-English paired records
4,096 general capability replay records
4,096 Qwen3.6 text and reasoning records

The observer covered all 40 MoE layers with a maximum sequence length of 8,192 tokens and seed 17. For every layer, routed experts were ranked using router-weighted activation statistics; the 192 highest-ranked experts were retained, the router rows were sliced to the same indices, and top-8 routing was preserved.

The calibration file and the complete per-layer/per-expert observation CSV are published in eouya2/KGuard-Korean-MoE-Calibration-v1.

Evaluation snapshot

The following scores use the same deterministic evaluation subsets and prompts for the base and pruned checkpoints.

Benchmark	Base 8-bit	This model
KMMLU	66.4	60.9
MMLU-Pro	64.3	58.8
GSM8K	96.7	96.7
HumanEval+ pass@1	90.9	93.3
Academic 10-benchmark macro	81.2	79.7

KMMLU, MMLU-Pro, and GSM8K use 512-item deterministic subsets. HumanEval+ uses the complete 164-task EvalPlus set. These results are intended for paired comparison under the reported local protocols rather than as direct official leaderboard submissions.

Use with MLX

Install MLX LM:

pip install -U mlx-lm

Generate from the command line:

bash

mlx_lm.generate \
  --model eouya2/Qwen3.6-35B-A3B-KGuard-REAP25-8bit-MLX \
  --prompt "한국어로 mixture-of-experts 모델을 간단히 설명해 주세요." \
  --max-tokens 256 \
  --temperature 0.0

Or use Python:

python

from mlx_lm import generate, load

model_id = "eouya2/Qwen3.6-35B-A3B-KGuard-REAP25-8bit-MLX"
model, tokenizer = load(model_id)

messages = [{"role": "user", "content": "한국어로 자기소개를 해 주세요."}]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

print(generate(model, tokenizer, prompt=prompt, max_tokens=256))

Serve an OpenAI-compatible endpoint:

mlx_lm.server --model eouya2/Qwen3.6-35B-A3B-KGuard-REAP25-8bit-MLX

Intended use

This checkpoint is suitable for local Apple Silicon experimentation, Korean and bilingual generation, MoE pruning research, and memory-conscious inference where a stronger reduction in routed-expert capacity is useful. As with the base model, application-specific safety and quality evaluation is recommended before deployment.

References and acknowledgements

Base model: Qwen/Qwen3.6-35B-A3B
MLX 8-bit conversion: mlx-community/Qwen3.6-35B-A3B-8bit
REAP paper: REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression
MLX pruning implementation: egesabanci/reap-mlx

License

The model follows the Apache 2.0 license of the upstream Qwen release. The companion synthetic calibration data is released separately under CC BY 4.0.