Darkrider/nirnaya-turbo-1.0

image-text-to-text model by Darkrider

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Nirnaya (निर्णय) means "decision" in Hindi. The Nirnaya family are decision models: give one a state and a set of questions, and it returns one answer per question with a probability on every option, in a single pass. Nirnaya Turbo 1.0 is the most accurate member of the family. It reads text, images and audio, in English and Indian languages. The figures above come from complete runs of the Decision Index kit. Private board scores are added here once they are published. The model needs the inference code in github.com/NeverInAsh/nirnaya-inference and the Decision Index kit. For the vision suite, use mmclm.diengine:NirnayaVisionEngine with maxsofttokens=560 and maximages=2. For audio, use mmclm.diengine:NirnayaAudioEngine. Install librosa fastapi uvicorn python-multipart, start the HTTP server with python -m mmclm.serve --model --port 8000, and send requests to POST /v1/decisions. The model was fine-tuned on a huge amount of data: about 69% text, 17% images and 14% audio, with…

Darkrider/nirnaya-turbo-1.0 on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Nirnaya Turbo 1.0
  2. How to run it
  3. Training data
  4. How we kept test data out of training
  5. Calibration
  6. Limits
  7. Using Nirnaya
  8. Licence

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Model overview

Darkrider/nirnaya-turbo-1.0 is a image-text-to-text model repository published by Darkrider on Hugging Face. The source reports the transformers library.

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Model facts

Task
image-text-to-text
Library
transformers
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
26,115,083,856
Architecture
Gemma4ForConditionalGeneration
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-11-10T06:32:48.000Z
Last modified
2026-11-10T06:34:37.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: google/gemma-4-26B-A4B-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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