Video-Text-to-Text
Transformers
Safetensors
English
internvl_chat
feature-extraction
multimodal
custom_code
Eval Results (legacy)
Instructions to use OpenGVLab/InternVideo2_5_Chat_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVideo2_5_Chat_8B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternVideo2_5_Chat_8B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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- Inference Speed
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We measured the average inference speed (tokens/s) of generating 1024 new tokens and 5198 (8192-2998) tokens with the context of an video (which takes 2998 tokens) under BF16 precision.
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|Quantization | Speed (3022 tokens) | Speed (8192 tokens) w/o
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|BF16 | 33.40 | 31.91 | 21.33|
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|INT4 | - | 31.95 | - |
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- Inference Speed
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We measured the average inference speed (tokens/s) of generating 1024 new tokens and 5198 (8192-2998) tokens with the context of an video (which takes 2998 tokens) under BF16 precision. w/ encoder indicates that the inference includes the time for video encoder.
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|Quantization | Speed (3022 tokens) | Speed (8192 tokens) w/o encoder| Speed(8192 tokens) w/ encoder|
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|BF16 | 33.40 | 31.91 | 21.33|
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|INT4 | - | 31.95 | - |
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