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 # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download mm_projector.bin from OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B: direct link, hf CLI and curl.
- Browser
- Download file 85.2 MB
-
https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/resolve/main/mm_projector.bin
- Command line
-
hf download hf://OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/mm_projector.bin
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curl -L -o mm_projector.bin https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/resolve/main/mm_projector.bin
85.2 MB
- Xet hash:
- 27c9cd5ed00d3672a83df1fce899061fc44989aeb79e235dad94b240ca300a88
- Size of remote file:
- 85.2 MB
- SHA256:
- f5c6b804c78f083f333e44a8720c3510202dbe707d038bac8fb306ee32e35bd5
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