Feature Extraction
Transformers
Safetensors
English
videollama3_qwen2
text-generation
video-retrieval
text-video-retrieval
video-text-retrieval
embedding
multimodal
videollama3
custom_code
Instructions to use iyttor/VidVec-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iyttor/VidVec-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="iyttor/VidVec-7B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("iyttor/VidVec-7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from iyttor/VidVec-7B: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/iyttor/VidVec-7B/resolve/main/processor_config.json
- Command line
-
hf download hf://iyttor/VidVec-7B/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/iyttor/VidVec-7B/resolve/main/processor_config.json
229 Bytes
| { | |
| "fps": 2, | |
| "image_merge_size": 1, | |
| "max_frames": 180, | |
| "processor_class": "Videollama3Qwen2Processor", | |
| "video_merge_size": 2, | |
| "auto_map": { | |
| "AutoProcessor": "processing_videollama3.Videollama3Qwen2Processor" | |
| } | |
| } | |