Improved Baselines with Visual Instruction Tuning
Authors: name: Haotian Liu; name: Chunyuan Li; name: Yuheng Li; name: Yong Jae Lee
Published: 2023-10-05 17:59:56

Abstract: Large multimodal models (LMM) have recently shown encouraging progress with
visual instruction tuning. In this note, we show that the fully-connected
vision-language cross-modal connector in LLaVA is surprisingly powerful and
data-efficient. With simple modifications to LLaVA, namely, using
CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA
data with simple response formatting prompts, we establish stronger baselines
that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint
uses merely 1.2M publicly available data, and finishes full training in ~1 day
on a single 8-A100 node. We hope this can make state-of-the-art LMM research
more accessible. Code and model will be publicly available.