An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Yadong Lu Chunyuan Li Haotian Liu Jianwei Yang Jianfeng Gao Yelong Shen
Visual instruction tuning has recently shown encouraging progress with
open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However,
most existing studies of open-source LMM are performed using models with 13B
parameters or smaller. In this paper we present an empirical study of scaling
LLaVA up to 33B and 65B/70B, and share our findings from our explorations in
image resolution, data mixing and parameter-efficient training methods such as
LoRA/QLoRA. These are evaluated by their impact on the multi-modal and language
capabilities when completing real-world tasks in the wild.
  We find that scaling LMM consistently enhances model performance and improves
language capabilities, and performance of LoRA/QLoRA tuning of LMM are
comparable to the performance of full-model fine-tuning. Additionally, the
study highlights the importance of higher image resolutions and mixing
multimodal-language data to improve LMM performance, and visual instruction
tuning can sometimes improve LMM's pure language capability. We hope that this
study makes state-of-the-art LMM research at a larger scale more accessible,
thus helping establish stronger baselines for future research. Code and
checkpoints will be made public.