Instructions to use brainworkup/LFM2.5-VL-3B-MLX-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/LFM2.5-VL-3B-MLX-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download brainworkup/LFM2.5-VL-3B-MLX-oQ8e --local-dir LFM2.5-VL-3B-MLX-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download processor_config.json from brainworkup/LFM2.5-VL-3B-MLX-oQ8e: direct link, hf CLI and curl.
- Browser
- Download file 662 Bytes
-
https://huggingface.co/brainworkup/LFM2.5-VL-3B-MLX-oQ8e/resolve/main/processor_config.json
- Command line
-
hf download hf://brainworkup/LFM2.5-VL-3B-MLX-oQ8e/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/brainworkup/LFM2.5-VL-3B-MLX-oQ8e/resolve/main/processor_config.json
662 Bytes
| { | |
| "image_processor": { | |
| "do_image_splitting": false, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "downsample_factor": 2, | |
| "encoder_patch_size": 16, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Lfm2VlNumpyImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_tokens": 256, | |
| "max_num_patches": 1024, | |
| "max_pixels_tolerance": 2.0, | |
| "min_image_tokens": 64, | |
| "patch_size": 16, | |
| "rescale_factor": 0.00392156862745098, | |
| "tile_size": 512, | |
| "use_thumbnail": false | |
| }, | |
| "processor_class": "Lfm2VlProcessor" | |
| } | |