Video-Text-to-Text
Transformers
Safetensors
MLX
English
molmo2
image-text-to-text
multimodal
olmo
molmo
custom_code
5-bit
Instructions to use mlx-community/Molmo2-8B-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/Molmo2-8B-5bit with Transformers:
# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("mlx-community/Molmo2-8B-5bit", trust_remote_code=True, dtype="auto") - MLX
How to use mlx-community/Molmo2-8B-5bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Molmo2-8B-5bit mlx-community/Molmo2-8B-5bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 984 Bytes
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"auto_map": {
"AutoProcessor": "processing_molmo2.Molmo2Processor",
"AutoVideoProcessor": "video_processing_molmo2.Molmo2VideoProcessor"
},
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": true,
"fps": null,
"frame_sample_mode": "uniform_last_frame",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"input_data_format": null,
"max_fps": 2.0,
"num_frames": 384,
"pad_size": null,
"patch_size": 14,
"pooling_size": [
3,
3
],
"processor_class": "Molmo2Processor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"sampling_fps": 2,
"size": {
"height": 378,
"width": 378
},
"video_metadata": null,
"video_processor_type": "Molmo2VideoProcessor"
}
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