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
File size: 503 Bytes
778d3bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"add_prefix_space": false,
"auto_map": {
"AutoProcessor": "processing_molmo2.Molmo2Processor"
},
"backend": "tokenizers",
"bos_token": "<|im_end|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": true,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"processor_class": "Molmo2Processor",
"split_special_tokens": false,
"tokenizer_class": "TokenizersBackend",
"unk_token": null
}
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