Instructions to use XMB480/Qwen3.6-fixed-tool-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XMB480/Qwen3.6-fixed-tool-calling with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XMB480/Qwen3.6-fixed-tool-calling",
	filename="Qwen3.6-27B-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 XMB480/Qwen3.6-fixed-tool-calling 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 XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XMB480/Qwen3.6-fixed-tool-calling: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 XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XMB480/Qwen3.6-fixed-tool-calling: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 XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

Use Docker

docker model run hf.co/XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

LM Studio
Jan

vLLM

How to use XMB480/Qwen3.6-fixed-tool-calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XMB480/Qwen3.6-fixed-tool-calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XMB480/Qwen3.6-fixed-tool-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

Ollama
How to use XMB480/Qwen3.6-fixed-tool-calling with Ollama:
```
ollama run hf.co/XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
```

Unsloth Studio

How to use XMB480/Qwen3.6-fixed-tool-calling 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 XMB480/Qwen3.6-fixed-tool-calling 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 XMB480/Qwen3.6-fixed-tool-calling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XMB480/Qwen3.6-fixed-tool-calling to start chatting

How to use XMB480/Qwen3.6-fixed-tool-calling with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XMB480/Qwen3.6-fixed-tool-calling: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": "XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use XMB480/Qwen3.6-fixed-tool-calling with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XMB480/Qwen3.6-fixed-tool-calling: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 XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use XMB480/Qwen3.6-fixed-tool-calling with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XMB480/Qwen3.6-fixed-tool-calling: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 "XMB480/Qwen3.6-fixed-tool-calling: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 XMB480/Qwen3.6-fixed-tool-calling with Docker Model Runner:
```
docker model run hf.co/XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M
```

Lemonade

How to use XMB480/Qwen3.6-fixed-tool-calling with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XMB480/Qwen3.6-fixed-tool-calling:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-fixed-tool-calling-Q4_K_M

List all available models

lemonade list

Qwen 27B - Hermes Optimized JSON Agent (GGUF)

This repository hosts a pre-configured deployment setup for the Qwen 27B model, specifically optimized to run as a local autonomous agent within structured tool-calling frameworks like Hermes.

The Problem Solved

When deploying local agent frameworks with standard Qwen GGUF files, the agent frequently breaks down during tool execution. The agent framework expects the model to communicate tool calls in strict, structured JSON formats.

However, the default chat template baked into standard Qwen models often causes the text generation to drift. The model regular attempts to wrap tool calls in raw XML blocks, markdown code wrappers, or outputs casual conversational text alongside the payload. This breaks the framework's parser, leading to continuous loop errors, missed execution arguments, and failed automation workflows.

The Fix

This configuration fixes the structural drift by overriding the default chat translation layer.

We updated the environment's chat_template.jinja file to explicitly inject strict JSON tool constraints (_tool_format = 'json'). This forces the underlying model weights to process inputs and format outputs into predictable, rock-solid JSON payloads, ensuring clean tool execution loop completion without breaking the parser.

Repository Contents

Qwen3.6-27B-Q4_K_M.gguf: The core text generation model quantized to 4-bit medium precision.
mmproj-Qwen3.6-27B-BF16.gguf: The multimodal vision projector block, allowing the text model to interpret images and screenshots via local browser automation tools.
chat_template.jinja: The custom Jinja template file that enforces the JSON formatting constraints.

How to Deploy and Use

Because GGUF engines default to using the template embedded inside the binary file, you must manually apply the custom Jinja template included in this repository to activate the fix.

Method 1: Linux / Arch Terminal (Recommended Engine)

For maximum efficiency and minimal resource overhead, run the model using raw llama.cpp. Pass the text weights, vision projector, and the custom template file directly via the terminal command:

llama-server -m Qwen3.6-27B-Q4_K_M.gguf --mmproj mmproj-Qwen3.6-27B-BF16.gguf --port 1234 -ctx 65000 -ngl 47 --chat-template-file chat_template.jinja

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for XMB480/Qwen3.6-fixed-tool-calling

Base model

Qwen/Qwen3.6-27B

Quantized

(599)

this model