Instructions to use aimeri/spoomplesmaxx-27b-4500-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aimeri/spoomplesmaxx-27b-4500-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("aimeri/spoomplesmaxx-27b-4500-mlx-4bit") config = load_config("aimeri/spoomplesmaxx-27b-4500-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
- Xet hash:
- 23db2e055d80cccf03598ded35b611c37cce36e1bc0bf93b87209e2a480a073d
- Size of remote file:
- 33.4 MB
- SHA256:
- daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
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