TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Authors: name: Zhengqing Yuan, name: Zhaoxu Li, name: Lichao Sun

In the era of advanced multimodel learning, multimodal large language models
(MLLMs) such as GPT-4V have made remarkable strides towards bridging language
and visual elements. However, the closed-source nature and considerable
computational demand present notable challenges for universal usage and
modifications. This is where open-source MLLMs like LLaVA and MiniGPT-4 come
in, presenting groundbreaking achievements across tasks. Despite these
accomplishments, computational efficiency remains an unresolved issue, as these
models, like LLaVA-v1.5-13B, require substantial resources. Addressing these
issues, we introduce TinyGPT-V, a new-wave model marrying impressive
performance with commonplace computational capacity. It stands out by requiring
merely a 24G GPU for training and an 8G GPU or CPU for inference. Built upon
Phi-2, TinyGPT-V couples an effective language backbone with pre-trained vision
modules from BLIP-2 or CLIP. TinyGPT-V's 2.8B parameters can undergo a unique
quantisation process, suitable for local deployment and inference tasks on 8G
various devices. Our work fosters further developments for designing
cost-effective, efficient, and high-performing MLLMs, expanding their
applicability in a broad array of real-world scenarios. Furthermore this paper
proposed a new paradigm of Multimodal Large Language Model via small backbones.
Our code and training weights are placed at:
https://github.com/DLYuanGod/TinyGPT-V and
https://huggingface.co/Tyrannosaurus/TinyGPT-V respectively.