Feature Extraction
Transformers
Safetensors
videojev
video
video-question-answering
temporal-grounding
custom_code
decision-model
Instructions to use ZhuYuhan/VideoJev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZhuYuhan/VideoJev-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ZhuYuhan/VideoJev-4B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZhuYuhan/VideoJev-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ZhuYuhan/VideoJev-4B: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/ZhuYuhan/VideoJev-4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://ZhuYuhan/VideoJev-4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ZhuYuhan/VideoJev-4B/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 753b193b9209fb4376860f8913da08bf2110e8ecdef626baf438886f7102e409
- Size of remote file:
- 20 MB
- SHA256:
- 0b16fb8cbf388f6af8691645a17c909c01e536b4eb1bad865ed05fb6ef4a642a
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