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 / chat_template.jinja

Commit History

Add fused MCP tool calling model (dequantized)

817e6a1
verified

pierretokns commited on Jan 27

Commit History

Add fused MCP tool calling model (dequantized) 817e6a1 verified

Add fused MCP tool calling model (dequantized)

817e6a1
verified