  We present the TinyLLaVA framework that provides a unified perspective in
designing and analyzing the small-scale Large Multimodal Models (LMMs). We
empirically study the effects of different vision encoders, connection modules,
language models, training data and training recipes. Our extensive experiments
showed that better quality of data combined with better training recipes,
smaller LMMs can consistently achieve on-par performances compared to bigger
LMMs. Under our framework, we train a family of small-scale LMMs. Our best
model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B
models such as LLaVA-1.5 and Qwen-VL. We hope our findings can serve as
baselines for future research in terms of data scaling, training setups and
model selections. Our model weights and codes will be made public.
