Instructions to use lmms-lab-encoder/onevision-encoder-large-lang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmms-lab-encoder/onevision-encoder-large-lang with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="lmms-lab-encoder/onevision-encoder-large-lang", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lmms-lab-encoder/onevision-encoder-large-lang", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from lmms-lab-encoder/onevision-encoder-large-lang: direct link, hf CLI and curl.
- Browser
- Download file 466 Bytes
-
https://huggingface.co/lmms-lab-encoder/onevision-encoder-large-lang/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://lmms-lab-encoder/onevision-encoder-large-lang@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/lmms-lab-encoder/onevision-encoder-large-lang/resolve/refs%2Fpr%2F1/preprocessor_config.json
466 Bytes
| { | |
| "crop_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 448 | |
| } | |
| } | |