Image-Text-to-Text
MLX
Safetensors
English
molmo_point
multimodal
olmo
molmo
molmo2
conversational
custom_code
Instructions to use mlx-community/MolmoPoint-8B-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/MolmoPoint-8B-fp16 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-fp16") config = load_config("mlx-community/MolmoPoint-8B-fp16") # 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: 346 Bytes
0a60033 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"auto_map": {
"AutoProcessor": "processing_molmo2.Molmo2Processor"
},
"image_use_col_tokens": true,
"processor_class": "Molmo2Processor",
"use_frame_special_tokens": true,
"use_low_res_token_for_global_crops": true,
"use_single_crop_col_tokens": false,
"use_single_crop_start_token": true,
"video_use_col_tokens": false
}
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