Image-Text-to-Text
MLX
Safetensors
English
Chinese
qwen3_5
apple-silicon
dwq
4-bit precision
vision-language
video
reasoning
tool-calling
mtp
crack
conversational
Instructions to use WaveCut/Qwen3.8-27B-CRACK-DWQ-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use WaveCut/Qwen3.8-27B-CRACK-DWQ-4bit-MLX 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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX") config = load_config("WaveCut/Qwen3.8-27B-CRACK-DWQ-4bit-MLX") # 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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX 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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX"
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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use WaveCut/Qwen3.8-27B-CRACK-DWQ-4bit-MLX 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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX"
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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use WaveCut/Qwen3.8-27B-CRACK-DWQ-4bit-MLX 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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX"
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/Qwen3.8-27B-CRACK-DWQ-4bit-MLX" \ --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"
| { | |
| "architectures": [ | |
| "Qwen3_5ForConditionalGeneration" | |
| ], | |
| "image_token_id": 248056, | |
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| "model_type": "qwen3_5", | |
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