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 pytorch_model-00002-of-00004.bin from OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B: direct link, hf CLI and curl.
- Browser
- Download file 9.9 GB
-
https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/resolve/main/pytorch_model-00002-of-00004.bin
- Command line
-
hf download hf://OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/pytorch_model-00002-of-00004.bin
-
curl -L -o pytorch_model-00002-of-00004.bin https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-13B/resolve/main/pytorch_model-00002-of-00004.bin
9.9 GB
- Xet hash:
- c272dd7af1c4a0bcf8c80ab198b002c62eebda9c2e27f2ad4f4d957752df0708
- Size of remote file:
- 9.9 GB
- SHA256:
- 86ec4bf105313ff70e8d37c70d65f99c5f1983340ba335435af3b523d72fb226
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