Instructions to use WaveCut/Qwen3.5-2B-MLX-DWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use WaveCut/Qwen3.5-2B-MLX-DWQ-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("WaveCut/Qwen3.5-2B-MLX-DWQ-4bit") config = load_config("WaveCut/Qwen3.5-2B-MLX-DWQ-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 WaveCut/Qwen3.5-2B-MLX-DWQ-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 "WaveCut/Qwen3.5-2B-MLX-DWQ-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": "WaveCut/Qwen3.5-2B-MLX-DWQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use WaveCut/Qwen3.5-2B-MLX-DWQ-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 "WaveCut/Qwen3.5-2B-MLX-DWQ-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 WaveCut/Qwen3.5-2B-MLX-DWQ-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use WaveCut/Qwen3.5-2B-MLX-DWQ-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 "WaveCut/Qwen3.5-2B-MLX-DWQ-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 "WaveCut/Qwen3.5-2B-MLX-DWQ-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"
Qwen3.5-2B — MLX DWQ 4-bit
Qwen3.5-2B for Apple silicon in MLX format. The language backbone uses 4-bit affine DWQ with selected 8-bit matrices, group size 32. The vision tower retains the original BF16 weights. Weight file: 1.90 GB.
Compatible with unmodified mlx-lm==0.31.3 and mlx-vlm==0.7.3, tested on stable mlx==0.32.2.
Quality
DWQ calibration uses multilingual natural text from FineWeb, FineWeb2 and Wikipedia, balanced across 16 languages. Source revision: 15852e8c16360a2fea060d615a32b45270f8a8fc.
On 65,408 held-out tokens across 16 languages, teacher KL is 21.4% lower than the 4-bit DWQ baseline without selective 8-bit matrices:
| Model | KL ↓ | Perplexity ↓ |
|---|---|---|
| DWQ 4-bit baseline | 0.08339 | 19.8703 |
| This model | 0.06554 | 19.6672 |
KL is measured against the BF16 source at temperature 1. These text metrics do not establish writing, instruction-following or vision quality. Image input passed a pipeline smoke test. Evaluation details.
Use
python -m pip install "mlx-vlm==0.7.3"
mlx_vlm.generate --model WaveCut/Qwen3.5-2B-MLX-DWQ-4bit \
--image /path/to/image.jpg --prompt "Describe this image." \
--max-tokens 128 --temperature 0
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