Instructions to use TSunm/InternVL2-1B-ViVQA-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TSunm/InternVL2-1B-ViVQA-X with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TSunm/InternVL2-1B-ViVQA-X", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0eb324f03cedc5e945e203d47595b265e855691f377d211bce80664c9840ebe4
- Size of remote file:
- 11.4 MB
- SHA256:
- 1d8ce419d8658c568ecdc7c3ccdaa4a32b8f5cb89df7469b822ad661eee5bdc2
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