Kotlin, Swift, Gradle, Coroutines, RxSwift, Jetpack Compose, Compose Multiplatform, SwiftUI, Decompose, Metro, Koin, Ktor, Room, Coil
English and Russian
Reasoning, instruction following, tool use / function calling
General knowledge (humanities)

What's removed

Other programming languages (Python, Java, Go, Rust, C, JS, PHP, Ruby, TS, HTML, bash, Qt)
Science/tech/esoteric domains (medicine, law, biology, chemistry, astronomy, physics, esoterics, cooking, dietetics)
All languages except English and Russian

Pruning method

Smart pruning: kept top-150 most active experts per layer (out of 256) based on heat data
Calibrated on 36 texts × 3 runs (Kotlin, Swift, RU/EN, SQL, architecture, reasoning, tool use, JSON/API) — 100% deterministic
Experts that don't activate on keep-texts are physically removed
Quantization: 3-bit (MLX)

Usage

MLX

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("siendsi/Qwen3-6-KMP-Dev-MLX-3bit")
response = generate(model, tokenizer, "Write a Kotlin function to parse JSON")
print(response)

LM Studio

Search for siendsi/Qwen3-6-KMP-Dev-MLX-3bit in LM Studio.

Variants

Variant	Format	Size	Quality
GGUF Q3_K_M	GGUF	9.7GB	~90% of original
GGUF Q2_K	GGUF	7.6GB	~75% of original
GGUF IQ1_M	GGUF	4.9GB	~60% of original, loops on tool use
MLX 3-bit	safetensors	11GB	~90% of original
MLX 2-bit	safetensors	6.3GB	~75% of original

Architecture

40 layers, hybrid: attention (every 4th) + SSM (Gated DeltaNet) + MoE
150 experts/layer, 8 active, 1 shared
embedding 2048, 16 heads, 2 KV heads, head_dim 256
rope dim 64, mrope [11,11,10,0], freq_base 10M