Instructions to use OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B") model = AutoModelForCausalLM.from_pretrained("OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a533d5a3bd990a186582d5eef992270a930a947951ffeb8c490732091b05f7b
- Size of remote file:
- 59.8 MB
- SHA256:
- 00bafdc63987d735b16ca2916a85a8038f220586d5e6cee07d2e18d77f0415d1
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