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 from transformers import pipeline pipe = pipeline("visual-question-answering", model="lym0302/VideoLLaMA2.1-7B-AV-CoT")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("lym0302/VideoLLaMA2.1-7B-AV-CoT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ae6f514c9a2503454ce660a3a51c3bf112ebb33dc52321a6bd91ebd1987cba7
- Size of remote file:
- 6.84 kB
- SHA256:
- 00760658f9340604217342ba0009dcc5678cd094d63e1198a060a02187a27cbc
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