Qwen3-6-KMP-Dev-MLX-2bit

Qwen3.6-35B-A3B KMP Dev (MLX 2-bit)

A pruned Qwen3.6-35B-A3B model optimized for Kotlin Multiplatform development and agentic tool use, converted to MLX 2-bit format.

License: Apache 2.0. Based on Qwen3.6-35B-A3B by Alibaba Cloud (Apache 2.0). Modified via expert pruning.

What's kept

• 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: 2-bit (MLX)

Usage

MLX

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("siendsi/Qwen3-6-KMP-Dev-MLX-2bit")
response = generate(model, tokenizer, "Write a Kotlin function to parse JSON")
print(response)

LM Studio

Search for siendsi/Qwen3-6-KMP-Dev-MLX-2bit in LM Studio.

Variants

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