Visual Question Answering
Transformers
Safetensors
English
videollama2_qwen2
text-generation
Audio-visual Question Answering
Audio Question Answering
multimodal large language model
Instructions to use DAMO-NLP-SG/VideoLLaMA2.1-7B-AV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VideoLLaMA2.1-7B-AV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="DAMO-NLP-SG/VideoLLaMA2.1-7B-AV")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/VideoLLaMA2.1-7B-AV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model-00004-of-00004.safetensors from DAMO-NLP-SG/VideoLLaMA2.1-7B-AV: direct link, hf CLI and curl.
- Browser
- Download file 2.25 GB
-
https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00004-of-00004.safetensors
- Command line
-
hf download hf://DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/model-00004-of-00004.safetensors
-
curl -L -o model-00004-of-00004.safetensors https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00004-of-00004.safetensors
2.25 GB
- Xet hash:
- 3327e5bd9436f2eb4e4b1f0fe6322dad367e635d65aec3d3cd84655cf3e4d821
- Size of remote file:
- 2.25 GB
- SHA256:
- ef0fd503c333dfd1f3a39bc802dcf2ac5045c61f1d04fbee1a60cf2b8d0e1b72
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