Instructions to use OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B 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-13B 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-13B")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B") model = AutoModelForCausalLM.from_pretrained("OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27c9cd5ed00d3672a83df1fce899061fc44989aeb79e235dad94b240ca300a88
- Size of remote file:
- 85.2 MB
- SHA256:
- f5c6b804c78f083f333e44a8720c3510202dbe707d038bac8fb306ee32e35bd5
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