Instructions to use spicyneuron/Qwen3.5-35B-A3B-MLX-4.9bit-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use spicyneuron/Qwen3.5-35B-A3B-MLX-4.9bit-vision 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("spicyneuron/Qwen3.5-35B-A3B-MLX-4.9bit-vision") config = load_config("spicyneuron/Qwen3.5-35B-A3B-MLX-4.9bit-vision") # 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
- Xet hash:
- 7d117ee50b2e82c7989ff157b87d58f665b136387e9ddadecc54b5e83ab0d1e2
- Size of remote file:
- 5.22 GB
- SHA256:
- 12c7e6da565518cb3d46b43802e931bb7470beb2c03164698f138329c3e3ea0c
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