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
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Verified signatureThis PR improves the model card by adding relevant YAML metadata (license, library name, and pipeline tag). It also links the repository to the primary research paper "LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence" and the associated technical report "OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence." Additionally, it includes links to the official GitHub repository and project page to improve discoverability and provides sample usage for both images and video.c353a2d
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