Text Classification
Transformers
Safetensors
gemma4_unified
image-text-to-text
multimodal
merged
decision-model
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
Commit History
22 days ago
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The Wall: 23-second demo film (11 cameras, voicemails, chats on one GPU)
Verified signaturecb4a0ff -
unified bf16 checkpoint: base Gemma 4 12B vision/audio + fine-tuned text model, one file; verified against the reference loader
Verified signaturec127654 -
Simplify model card and add merged Jev-Omni Medium accuracy and calibration plots
Verified signature55b53f2 -
Update README.md
Verified signature2ef0877 -
Jev-Omni: verified public release
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