SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for
  Multi-modal Large Language Models
Authors: name: Ziyi Lin, name: Chris Liu, name: Renrui Zhang, name: Peng Gao, name: Longtian Qiu, name: Han Xiao, name: Han Qiu, name: Chen Lin, name: Wenqi Shao, name: Keqin Chen, name: Jiaming Han, name: Siyuan Huang, name: Yichi Zhang, name: Xuming He, name: Hongsheng Li, name: Yu Qiao

We present SPHINX, a versatile multi-modal large language model (MLLM) with a
joint mixing of model weights, tuning tasks, and visual embeddings. First, for
stronger vision-language alignment, we unfreeze the large language model (LLM)
during pre-training, and introduce a weight mix strategy between LLMs trained
by real-world and synthetic data. By directly integrating the weights from two
domains, the mixed LLM can efficiently incorporate diverse semantics with
favorable robustness. Then, to enable multi-purpose capabilities, we mix a
variety of tasks for joint visual instruction tuning, and design task-specific
instructions to avoid inter-task conflict. In addition to the basic visual
question answering, we include more challenging tasks such as region-level
understanding, caption grounding, document layout detection, and human pose
estimation, contributing to mutual enhancement over different scenarios.
Additionally, we propose to extract comprehensive visual embeddings from
various network architectures, pre-training paradigms, and information
granularity, providing language models with more robust image representations.
Based on our proposed joint mixing, SPHINX exhibits superior multi-modal
understanding capabilities on a wide range of applications. On top of this, we
further propose an efficient strategy aiming to better capture fine-grained
appearances of high-resolution images. With a mixing of different scales and
high-resolution sub-images, SPHINX attains exceptional visual parsing and
reasoning performance on existing evaluation benchmarks. We hope our work may
cast a light on the exploration of joint mixing in future MLLM research. Code
is released at https://github.com/Alpha-VLLM/LLaMA2-Accessory.