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 # 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-7B")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B/resolve/main/tokenizer.model
- Command line
-
hf download hf://OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/OpenGVLab/InternVL-Chat-ViT-6B-Vicuna-7B/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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