Image-Text-to-Text
MLX
Safetensors
qwen3_5
qwen3.5
vision-language-model
quantized
4bit
conversational
4-bit precision
Instructions to use mlx-community/Qwen3.5-9B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen3.5-9B-MLX-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/Qwen3.5-9B-MLX-4bit") config = load_config("mlx-community/Qwen3.5-9B-MLX-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
- LM Studio
- Pi new
How to use mlx-community/Qwen3.5-9B-MLX-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/Qwen3.5-9B-MLX-4bit"
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": "mlx-community/Qwen3.5-9B-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Qwen3.5-9B-MLX-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/Qwen3.5-9B-MLX-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/Qwen3.5-9B-MLX-4bit
Run Hermes
hermes
Qwen3.5-9B-MLX-4bit
This is a quantized MLX version of Qwen/Qwen3.5-9B for Apple Silicon.
Model Details
- Original Model: Qwen/Qwen3.5-9B
- Quantization: 4-bit (~5.059 bits per weight)
- Group Size: 64
- Format: MLX SafeTensors
- Framework: mlx-vlm
Conversion Details
This model was converted using mlx-vlm with 4-bit quantization.
Conversion command:
python3 -m mlx_vlm convert \
--hf-path "Qwen/Qwen3.5-9B" \
--mlx-path "./mlx_models/Qwen3.5-9B-MLX-4bit" \
-q --q-bits 4 --q-group-size 64
Important Note
A better, more optimized conversion may be available from @Prince (@Blaizzy) in the MLX VLM community. Check the mlx-community organization for updated versions as official Qwen3.5 support is merged into the main mlx-vlm branch.
Usage
from mlx_vlm import load, generate
model, processor = load("mlx-community/Qwen3.5-9B-MLX-4bit")
output = generate(
model,
processor,
prompt="Describe this image in detail",
image="path/to/image.jpg",
max_tokens=200
)
print(output)
Or from the command line:
mlx_vlm generate \
--model mlx-community/Qwen3.5-9B-MLX-4bit \
--prompt "Describe this image" \
--image path/to/image.jpg \
--max-tokens 200
Performance
- Disk Size: ~5.6 GB
- Runs efficiently on Apple Silicon Macs (M1/M2/M3/M4)
- Lower memory footprint compared to 8-bit quantization
License
This model inherits the Apache 2.0 license from the original Qwen3.5-9B model.
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Model size
2B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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4-bit