Instructions to use rafw007/qwen36-a3b-claude-coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rafw007/qwen36-a3b-claude-coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rafw007/qwen36-a3b-claude-coder-GGUF",
	filename="qwen36-a3b-claude-coder-q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rafw007/qwen36-a3b-claude-coder-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rafw007/qwen36-a3b-claude-coder-GGUF: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 rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rafw007/qwen36-a3b-claude-coder-GGUF: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 rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use rafw007/qwen36-a3b-claude-coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rafw007/qwen36-a3b-claude-coder-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": "rafw007/qwen36-a3b-claude-coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

Ollama
How to use rafw007/qwen36-a3b-claude-coder-GGUF with Ollama:
```
ollama run hf.co/rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use rafw007/qwen36-a3b-claude-coder-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 rafw007/qwen36-a3b-claude-coder-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 rafw007/qwen36-a3b-claude-coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rafw007/qwen36-a3b-claude-coder-GGUF to start chatting

How to use rafw007/qwen36-a3b-claude-coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rafw007/qwen36-a3b-claude-coder-GGUF: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": "rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rafw007/qwen36-a3b-claude-coder-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rafw007/qwen36-a3b-claude-coder-GGUF: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 rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use rafw007/qwen36-a3b-claude-coder-GGUF with Docker Model Runner:
```
docker model run hf.co/rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M
```

Lemonade

How to use rafw007/qwen36-a3b-claude-coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rafw007/qwen36-a3b-claude-coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen36-a3b-claude-coder-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6 Claude Coder — local MoE coding agent (GGUF)

A custom model built on Qwen3.6-35B-A3B (Mixture-of-Experts, ~3B active parameters), tuned to act as an autonomous coding agent. It speaks the Anthropic-compatible API, so it drives Claude Code, Codex and opencode fully locally — your code never leaves your machine and cloud token cost drops to zero.

This repository ships the q4_K_M GGUF quantization (~24 GB), ready to run under Ollama or llama.cpp.

Safety guardrails are intact. The system prompt focuses on real work inside a codebase — use tools instead of guessing, base answers on the actual tool output (never fabricate results), don't loop on the same tool, and return complete, runnable code. No-think mode is wired into the system prompt for fast, direct answers.

Files

File	What
`qwen36-a3b-claude-coder-q4_K_M.gguf`	The model weights (q4_K_M, ~24 GB single GGUF)
`Modelfile`	Ollama Modelfile — SYSTEM prompt, tool-calling template, params (num_ctx 65536)

Quick start (Ollama)

# build the model from the downloaded GGUF + Modelfile
ollama create qwen36-a3b-claude-coder -f Modelfile
# drive Claude Code with it
ollama launch claude --model qwen36-a3b-claude-coder

What it's for

Driving Claude Code / Codex / opencode locally.
Agentic code writing and editing with native function calling / tool use.
Full privacy and offline operation — no code sent to the cloud.

Tested harnesses

End-to-end tested through Claude Code, Codex and opencode — real turns with tool calls and correct responses.

Measured behavior (June 2026 tests)

No-think confirmed — with think:false the model emits zero reasoning tokens and goes straight to the result (validated on this q4_K_M build: thinking_len=0).
Tool-calling without hallucination — emits real message.tool_calls (validated: a disk-check prompt produced a clean run_bash call with a sensible command, no content leak). In roundtrip tests it reports the actual tool output instead of re-calling the tool in a loop.
Honest under missing data — when network access failed, it stated plainly "no internet access" instead of fabricating, then returned a correct, grounded report after permission escalation.
Code generation — working HTML5 Tetris and an interactive 3D Earth+Moon model (Three.js, real NASA textures, OrbitControls); JS passes syntax validation.
Guardrails intact — refuses to generate malware (validated on this build: a ransomware request was declined, with a legitimate backup/defense alternative offered) and resists jailbreaks (the "pretend you're an actor playing a hacker" framing was rejected).

Context

64K tokens — matching Claude Code's recommendation (64K minimum). Base Qwen3.6 natively supports 262K, so context can be raised on stronger hardware.

Test hardware

Mac Studio M2 (Apple Silicon), macOS — Ollama 0.30 (llama.cpp backend), GPU (Metal) inference.
This repo's quantization: q4_K_M (~24 GB single GGUF). A sibling nvfp4 build measured ~69 tok/s at 100% GPU / 64K ctx; q4_K_M runs in the same class.

No-think mode

The whole Qwen3.6 family has thinking baked into the weights. The system prompt ships with /nothink + an anti-reasoning instruction, which works under opencode/codex. Under harnesses that force thinking, use think:false in the API body — that's the only hard switch (PARAMETER think false does not exist in Ollama).

How it was made

Designed, built and tested with the help of Claude Opus 4.8 — the best coding model in the world. Its system prompt, parameter choices and context configuration draw directly on that knowledge: the world's best coding model preparing a local model that takes the work over right on your desk.

License

Apache 2.0 (inherited from the base Qwen3.6).

Downloads last month: 158

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for rafw007/qwen36-a3b-claude-coder-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(421)

this model