Instructions to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("siendsi/Qwen3-6-KMP-Dev-MLX-2bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "siendsi/Qwen3-6-KMP-Dev-MLX-2bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "siendsi/Qwen3-6-KMP-Dev-MLX-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use siendsi/Qwen3-6-KMP-Dev-MLX-2bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "siendsi/Qwen3-6-KMP-Dev-MLX-2bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default siendsi/Qwen3-6-KMP-Dev-MLX-2bit

Run Hermes

hermes

Atomic Chat

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

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 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

Downloads last month: 123

MLX

Hardware compatibility

Quantized

Model tree for siendsi/Qwen3-6-KMP-Dev-MLX-2bit

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(210)

this model