Visual Question Answering
Transformers
Safetensors
English
videollama3_qwen2
text-generation
multi-modal
large-language-model
video-language-model
custom_code
Instructions to use DAMO-NLP-SG/VideoLLaMA3-2B-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DAMO-NLP-SG/VideoLLaMA3-2B-Image 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/VideoLLaMA3-2B-Image", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-SG/VideoLLaMA3-2B-Image", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from DAMO-NLP-SG/VideoLLaMA3-2B-Image: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-2B-Image/resolve/main/processor_config.json
- Command line
-
hf download hf://DAMO-NLP-SG/VideoLLaMA3-2B-Image/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/DAMO-NLP-SG/VideoLLaMA3-2B-Image/resolve/main/processor_config.json
229 Bytes
| { | |
| "auto_map": { | |
| "AutoProcessor": "processing_videollama3.Videollama3Qwen2Processor" | |
| }, | |
| "fps": 1, | |
| "image_merge_size": 1, | |
| "max_frames": 128, | |
| "processor_class": "Videollama3Qwen2Processor", | |
| "video_merge_size": 2 | |
| } | |