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
Update config.json
Browse files- config.json +1 -1
config.json
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -4,
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"mm_vision_tower": "
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"model_type": "llava",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -4,
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"mm_vision_tower": "OpenGVLab/InternViT-6B-224px",
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"model_type": "llava",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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