- Libraries
- MLX
How to use mlx-community/Qwen3-VL-4B-Instruct-4bit with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("mlx-community/Qwen3-VL-4B-Instruct-4bit")
config = load_config("mlx-community/Qwen3-VL-4B-Instruct-4bit")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use mlx-community/Qwen3-VL-4B-Instruct-4bit with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen3-VL-4B-Instruct-4bit"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "mlx-community/Qwen3-VL-4B-Instruct-4bit"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Hermes Agent
How to use mlx-community/Qwen3-VL-4B-Instruct-4bit 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 "mlx-community/Qwen3-VL-4B-Instruct-4bit"
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 mlx-community/Qwen3-VL-4B-Instruct-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mlx-community/Qwen3-VL-4B-Instruct-4bit with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen3-VL-4B-Instruct-4bit"
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 "mlx-community/Qwen3-VL-4B-Instruct-4bit" \
--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"
