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-00003-of-00004.safetensors from DAMO-NLP-SG/VideoLLaMA2.1-7B-AV: direct link, hf CLI and curl.
- Browser
- Download file 4.99 GB
-
https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00003-of-00004.safetensors
- Command line
-
hf download hf://DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/model-00003-of-00004.safetensors
-
curl -L -o model-00003-of-00004.safetensors https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00003-of-00004.safetensors
4.99 GB
- Xet hash:
- b9a2beebad3d7f73a0227d56fbdf0a42a48e0e42e0a642606cf1227ab48d2fb3
- Size of remote file:
- 4.99 GB
- SHA256:
- bee352a9285a0cea9c74d44a1b6c77cb15953f22b0fbbcd05966eeb9c3cbee7e
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