Visual Question Answering
Transformers
Safetensors
English
videollama2_qwen2
text-generation
Audio-visual Question Answering
Audio Question Answering
multimodal large language model
Instructions to use lym0302/VideoLLaMA2.1-7B-AV-CoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lym0302/VideoLLaMA2.1-7B-AV-CoT 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="lym0302/VideoLLaMA2.1-7B-AV-CoT")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("lym0302/VideoLLaMA2.1-7B-AV-CoT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lym0302/VideoLLaMA2.1-7B-AV-CoT: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/lym0302/VideoLLaMA2.1-7B-AV-CoT/resolve/main/training_args.bin
- Command line
-
hf download hf://lym0302/VideoLLaMA2.1-7B-AV-CoT/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lym0302/VideoLLaMA2.1-7B-AV-CoT/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 4ae6f514c9a2503454ce660a3a51c3bf112ebb33dc52321a6bd91ebd1987cba7
- Size of remote file:
- 6.84 kB
- SHA256:
- 00760658f9340604217342ba0009dcc5678cd094d63e1198a060a02187a27cbc
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