Image-Text-to-Text
MLX
Safetensors
English
molmo_point
multimodal
olmo
molmo
molmo2
conversational
custom_code
4-bit precision
Instructions to use mlx-community/MolmoPoint-8B-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/MolmoPoint-8B-nvfp4 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/MolmoPoint-8B-nvfp4") config = load_config("mlx-community/MolmoPoint-8B-nvfp4") # 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:
- d075aa712cf0d04db00efa65aa214cbd528cc3378f6aa1f7134a613b27de9a3c
- Size of remote file:
- 17.4 MB
- SHA256:
- 2f437033cf8ca3315943460f7b7681d01130795107d9a99dc124fd9d6898e932
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