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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lmms-lab-encoder/onevision-encoder-large-lang", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 466 Bytes
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"do_resize": true,
"image_mean": [
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"image_processor_type": "CLIPImageProcessor",
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