Instructions to use akhilaaa3/Jev-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhilaaa3/Jev-Omni with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akhilaaa3/Jev-Omni")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("akhilaaa3/Jev-Omni") model = AutoModelForMultimodalLM.from_pretrained("akhilaaa3/Jev-Omni", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 389 Bytes
22d65eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 | from jev_omni import load_jev_omni
classifier = load_jev_omni()
# Text. For other modalities, set modality="image"/"audio"/"video" and
# media="/path/to/file". Video is sampled to 16 frames; audio is capped at 30 s.
result = classifier.predict(
state="The meeting starts at 10 AM. It is now 9 AM.",
question="Has the meeting started?",
options=["Yes", "No"],
)
print(result)
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