Visual Question Answering
Transformers
Safetensors
English
videollama2_qwen2
text-generation
Audio-visual Question Answering
Audio Question Answering
multimodal large language model
Eval Results
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 # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="DAMO-NLP-SG/VideoLLaMA2.1-7B-AV")# pip install -U transformers accelerate # 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 tokenizer.json from DAMO-NLP-SG/VideoLLaMA2.1-7B-AV: direct link, hf CLI and curl.
- Browser
- Download file 7.03 MB
-
https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/tokenizer.json
- Command line
-
hf download hf://DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/DAMO-NLP-SG/VideoLLaMA2.1-7B-AV/resolve/main/tokenizer.json
7.03 MB
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