# MANTA -- Model Adapter Native generations that's Affordable

Source: [MANTA -- Model Adapter Native generations that's Affordable](<https://www.sota2.com/research/paper/68fa74ea1e209131cea64b28>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:22:19.696Z

Authors: Ansh Chaurasia

Published: 2024-09-22T08:38:23.000Z

## Abstract

The presiding model generation algorithms rely on simple, inflexible adapter selection to provide personalized results. We propose the model-adapter composition problem as a generalized problem to past work factoring in practical hardware and affordability constraints, and introduce MANTA as a new approach to the problem. Experiments on COCO 2014 validation show MANTA to be superior in image task diversity and quality at the cost of a modest drop in alignment. Our system achieves a $94\\%$ win rate in task diversity and a $80\\%$ task quality win rate versus the best known system, and demonstrates strong potential for direct use in synthetic data generation and the creative art domains.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.14363>)

[PDF](<https://arxiv.org/pdf/2409.14363.pdf>)