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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "siendsi/Qwen3-6-KMP-Dev-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "siendsi/Qwen3-6-KMP-Dev-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Ollama
How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Ollama:
```
ollama run hf.co/siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
```

Unsloth Studio

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for siendsi/Qwen3-6-KMP-Dev-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for siendsi/Qwen3-6-KMP-Dev-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for siendsi/Qwen3-6-KMP-Dev-GGUF to start chatting

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

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-GGUF:UD-Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Docker Model Runner
How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Docker Model Runner:
```
docker model run hf.co/siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M
```

Lemonade

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-6-KMP-Dev-GGUF-UD-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use siendsi/Qwen3-6-KMP-Dev-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf siendsi/Qwen3-6-KMP-Dev-GGUF:UD-Q4_K_M

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-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.6-35B-A3B KMP Dev (pruned)

A pruned Qwen3.6-35B-A3B model optimized for Kotlin Multiplatform development.

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
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-110 most active experts per layer (out of 256) based on heat data
Experts that don't activate on keep-texts are physically removed
Quantization: Q4_K_M (9.5GB)

Variants

Variant	Format	Size	Quality
GGUF Q4_K_M	GGUF	9.5GB	~90% of original
MLX 4-bit	safetensors	~8GB	~90% of original

Usage

LM Studio: lms get siendsi/qwen3-6-kmp-dev
llama.cpp: llama-server -m Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 16384
MLX: mlx_lm.generate --model siendsi/Qwen3-6-KMP-Dev-MLX-4bit

Architecture

40 layers, hybrid: attention (every 4th) + SSM (Gated DeltaNet) + MoE
110 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: -

GGUF

Model size

16B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for siendsi/Qwen3-6-KMP-Dev-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(736)

this model