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
Download assets/medium-accuracy.png from akhilaaa3/Jev-Omni: direct link, hf CLI and curl.
- Browser
- Download file 79.9 kB
-
https://huggingface.co/akhilaaa3/Jev-Omni/resolve/main/assets/medium-accuracy.png
- Command line
-
hf download hf://akhilaaa3/Jev-Omni/assets/medium-accuracy.png
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curl -L -o medium-accuracy.png https://huggingface.co/akhilaaa3/Jev-Omni/resolve/main/assets/medium-accuracy.png
79.9 kB

- Xet hash:
- 12dc04128f2cb8f1b906c7f4a69d21ce897ab44d1719022bd08a4a09a62f63cf
- Size of remote file:
- 79.9 kB
- SHA256:
- 9b7de696d8b833130a9aa6cf2a0d88e78eef216a9ecbb8fe336781fafabdb618
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