OmniFusion Technical Report
<|reference_start|>OmniFusion Technical Report: Last year, multimodal architectures served up a revolution in AI-based approaches and solutions, extending the capabilities of large language models (LLM). We propose an \textit{OmniFusion} model based on a pretrained LLM and adapters for visual modality. We evaluated and compared several architecture design principles for better text and visual data coupling: MLP and transformer adapters, various CLIP ViT-based encoders (SigLIP, InternVIT, etc.), and their fusing approach, image encoding method (whole image or tiles encoding) and two 7B LLMs (the proprietary one and open-source Mistral). Experiments on 8 visual-language benchmarks show the top score for the best OmniFusion setup in terms of different VQA tasks in comparison with open-source LLaVA-like solutions: VizWiz, Pope, MM-Vet, ScienceQA, MMBench, TextVQA, VQAv2, MMMU. We also propose a variety of situations, where OmniFusion provides highly-detailed answers in different domains: housekeeping, sightseeing, culture, medicine, handwritten and scanned equations recognition, etc. Mistral-based OmniFusion model is an open-source solution with weights, training and inference scripts available at https://github.com/AIRI-Institute/OmniFusion.<|reference_end|>
@article{goncharova2024omnifusion,
    title={OmniFusion Technical Report},
    author={Elizaveta Goncharova, Anton Razzhigaev, Matvey Mikhalchuk, Maxim
  Kurkin, Irina Abdullaeva, Matvey Skripkin, Ivan Oseledets, Denis Dimitrov and
  Andrey Kuznetsov},
    journal={arXiv preprint arXiv:2404.06212},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06212},
    primaryClass={cs.CV cs.AI cs.LG}
}