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TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models

About

We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources.

Junlong Jia, Ying Hu, Xi Weng, Yiming Shi, Miao Li, Xingjian Zhang, Baichuan Zhou, Ziyu Liu, Jie Luo, Lei Huang, Ji Wu• 2024

Related benchmarks

TaskDatasetResultRank
Visual Question AnsweringVQA v2 (test-dev)
Overall Accuracy80.1
664
Multimodal UnderstandingMMMU (val)
MMMU Score38.4
111
Multimodal PerceptionMME Perception--
61
Science Question AnsweringScienceQA IMG (test)
Accuracy73
45
Visual Question AnsweringGQA balanced (test-dev)
Accuracy62.1
32
Object Hallucination EvaluationPOPE MSCOCO (val)
F1 Score87.2
21
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