Instructions to use pierretokns/qwen-0.5b-ccmcp-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pierretokns/qwen-0.5b-ccmcp-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pierretokns/qwen-0.5b-ccmcp-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pierretokns/qwen-0.5b-ccmcp-v1")
model = AutoModelForCausalLM.from_pretrained("pierretokns/qwen-0.5b-ccmcp-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use pierretokns/qwen-0.5b-ccmcp-v1 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("pierretokns/qwen-0.5b-ccmcp-v1")

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)

llama-cpp-python

How to use pierretokns/qwen-0.5b-ccmcp-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pierretokns/qwen-0.5b-ccmcp-v1",
	filename="qwen-mcp-f16.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pierretokns/qwen-0.5b-ccmcp-v1 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 pierretokns/qwen-0.5b-ccmcp-v1:F16
# Run inference directly in the terminal:
llama cli -hf pierretokns/qwen-0.5b-ccmcp-v1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pierretokns/qwen-0.5b-ccmcp-v1:F16
# Run inference directly in the terminal:
llama cli -hf pierretokns/qwen-0.5b-ccmcp-v1:F16

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 pierretokns/qwen-0.5b-ccmcp-v1:F16
# Run inference directly in the terminal:
./llama-cli -hf pierretokns/qwen-0.5b-ccmcp-v1:F16

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 pierretokns/qwen-0.5b-ccmcp-v1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pierretokns/qwen-0.5b-ccmcp-v1:F16

Use Docker

docker model run hf.co/pierretokns/qwen-0.5b-ccmcp-v1:F16

LM Studio
Jan

vLLM

How to use pierretokns/qwen-0.5b-ccmcp-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pierretokns/qwen-0.5b-ccmcp-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pierretokns/qwen-0.5b-ccmcp-v1:F16

SGLang

How to use pierretokns/qwen-0.5b-ccmcp-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pierretokns/qwen-0.5b-ccmcp-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pierretokns/qwen-0.5b-ccmcp-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pierretokns/qwen-0.5b-ccmcp-v1 with Ollama:
```
ollama run hf.co/pierretokns/qwen-0.5b-ccmcp-v1:F16
```

Unsloth Studio

How to use pierretokns/qwen-0.5b-ccmcp-v1 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 pierretokns/qwen-0.5b-ccmcp-v1 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 pierretokns/qwen-0.5b-ccmcp-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pierretokns/qwen-0.5b-ccmcp-v1 to start chatting

How to use pierretokns/qwen-0.5b-ccmcp-v1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pierretokns/qwen-0.5b-ccmcp-v1"

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": "pierretokns/qwen-0.5b-ccmcp-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pierretokns/qwen-0.5b-ccmcp-v1 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 "pierretokns/qwen-0.5b-ccmcp-v1"

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 pierretokns/qwen-0.5b-ccmcp-v1

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use pierretokns/qwen-0.5b-ccmcp-v1 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pierretokns/qwen-0.5b-ccmcp-v1"

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 "pierretokns/qwen-0.5b-ccmcp-v1" \
  --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 pierretokns/qwen-0.5b-ccmcp-v1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pierretokns/qwen-0.5b-ccmcp-v1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pierretokns/qwen-0.5b-ccmcp-v1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pierretokns/qwen-0.5b-ccmcp-v1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use pierretokns/qwen-0.5b-ccmcp-v1 with Docker Model Runner:
```
docker model run hf.co/pierretokns/qwen-0.5b-ccmcp-v1:F16
```

Lemonade

How to use pierretokns/qwen-0.5b-ccmcp-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pierretokns/qwen-0.5b-ccmcp-v1:F16

Run and chat with the model

lemonade run user.qwen-0.5b-ccmcp-v1-F16

List all available models

lemonade list

qwen-0.5b-ccmcp-v1

Qwen 0.5B trained for Claude Chrome MCP tool calling

Attribution: Built with Qwen

Model Description

Fine-tuned for MCP (Model Context Protocol) tool calling with the Claude Chrome extension. The model generates tool calls for browser automation tasks.

Training Details

Base Model: Qwen/Qwen2.5-Coder-0.5B-Instruct
Method: LoRA fine-tuning on Apple Silicon (MLX)
Dataset: 1,782 MCP browser automation examples
Validation Loss: 0.083
Iterations: 500
Naming Convention: {base}-{size}-ccmcp-{version}
- ccmcp = Claude Chrome MCP

Files

adapters.safetensors - LoRA adapter weights
adapter_config.json - LoRA configuration
qwen-0.5b-ccmcp-v1-f16.gguf - GGUF F16 format for llama.cpp/Ollama
checkpoints/ - Training checkpoints

Usage

With MLX (Apple Silicon)

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load(
    "mlx-community/Qwen2.5-Coder-0.5B-Instruct-4bit",
    adapter_path="pierretokns/qwen-0.5b-ccmcp-v1"
)

messages = [
    {"role": "system", "content": "You are a browser automation assistant with MCP tools."},
    {"role": "user", "content": "Go to google.com"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
sampler = make_sampler(temp=0.1)

response = generate(model, tokenizer, prompt=prompt, max_tokens=150, sampler=sampler)
print(response)

With Ollama

# Download GGUF from this repo
# Create Modelfile:
cat > Modelfile << 'EOF'
FROM ./qwen-0.5b-ccmcp-v1-f16.gguf
PARAMETER num_ctx 8192
PARAMETER temperature 0.1
SYSTEM "You are a browser automation assistant with MCP tools."
EOF

# Create and run
ollama create qwen-0.5b-ccmcp-v1 -f Modelfile
ollama run qwen-0.5b-ccmcp-v1 "Go to google.com"

With Claude Code + Ollama

ANTHROPIC_BASE_URL=http://localhost:11434 \
ANTHROPIC_AUTH_TOKEN=ollama \
ANTHROPIC_API_KEY=ollama \
claude --model qwen-0.5b-ccmcp-v1

MCP Tools

The model was trained on 16 MCP browser automation tools: navigate, read_page, find, computer, form_input, get_page_text, screenshot, javascript_tool, tabs_context_mcp, tabs_create_mcp, gif_creator, upload_image, read_console_messages, read_network_requests, shortcuts_list, shortcuts_execute

License

This model is licensed under the Apache 2.0 License.

Downloads last month: 76

Safetensors

Model size

0.5B params

Tensor type

F16

MLX

Hardware compatibility

Quantized

Model tree for pierretokns/qwen-0.5b-ccmcp-v1

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B

Finetuned

Qwen/Qwen2.5-Coder-0.5B-Instruct

Adapter

(52)

this model