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-00002-of-00004.safetensors from DAMO-NLP-SG/VideoLLaMA2.1-7B-AV: direct link, hf CLI and curl.
- Browser
- Download file 4.93 GB
-
https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00002-of-00004.safetensors
- Command line
-
hf download hf://DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/model-00002-of-00004.safetensors
-
curl -L -o model-00002-of-00004.safetensors https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/model-00002-of-00004.safetensors
4.93 GB
- Xet hash:
- 275ef87488f760ff50af5433b3f4e8013189e0ff47f78793f190c7b517cf8038
- Size of remote file:
- 4.93 GB
- SHA256:
- 11b1ce6b035d2422d562c6c8bc1680a097d92e6001ed181ec488c279bb83dfe9
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