akhilaaa3/Jev-Omni

text-classification model by akhilaaa3

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A multimodal decision classifier for text, images, audio and video. Supply a question and options; receive a probability for each option—not a generated explanation. Built on Gemma 4 12B IT, with a 30,000-question fine-tuning run. Merged-model results. ¹Equal-weight scenario/group average. Reference scores are officially reported by their developers and may use different evaluation protocols. Jev-Omni cost uses its recorded input tokens at OpenRouter's Gemma 3 12B input rate ($0.05/M); its classifier generates no output tokens. Both classifiers are priced one call per question: a classifier answers one question at a time, so the state is re-sent for each of them and there is no discount for asking several at once. The three chat models are priced one call per state, with all of that state's questions together, which is their own cheapest shape. Splitting a state into per-question calls multiplies input tokens by 2.82x on this set, so pricing the…

akhilaaa3/Jev-Omni on Hugging Face A short preview, not the full document.

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  1. Jev-Omni
  2. Results
  3. Open-weight comparison
  4. Quick start
  5. Speed
  6. Limits
  7. License
  8. Calibration

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Model overview

akhilaaa3/Jev-Omni is a text-classification model repository published by akhilaaa3 on Hugging Face. The source reports the transformers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
text-classification
Library
transformers
Recent downloads (30 days)
0
Cumulative likes
96
Hugging Face trending score
95
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-09-20T21:43:20.000Z
Last modified
2026-09-22T22:45:48.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: google/gemma-4-12B-it

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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Source: Hugging Face Hub. Metadata observed 2026-09-23T06:37:23.610Z. Daily imports are snapshots, not real-time monitoring.

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