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arXiv · 2406.18193

MammothModa: Multi-Modal Large Language Model

Qi She·Junwen Pan·Xin Wan·Rui Zhang·Dawei Lu·Kai Huang
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Abstract

In this report, we introduce MammothModa, yet another multi-modal large language model (MLLM) designed to achieve state-of-the-art performance starting from an elementary baseline. We focus on three key design insights: (i) Integrating Visual Capabilities while Maintaining Complex Language Understanding: In addition to the vision encoder, we incorporated the Visual Attention Experts into the LLM to enhance its visual capabilities. (ii) Extending Context Window for High-Resolution and Long-Duration Visual Feature: We explore the Visual Merger Module to effectively reduce the token number of high-resolution images and incorporated frame position ids to avoid position interpolation. (iii) High-Quality Bilingual Datasets: We meticulously curated and filtered a high-quality bilingual multimodal dataset to reduce visual hallucinations. With above recipe we build MammothModa that consistently outperforms the state-of-the-art models, e.g., LLaVA-series, across main real-world visual language benchmarks without bells and whistles.

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Qi She, Junwen Pan, Xin Wan, Rui Zhang, Dawei Lu, Kai Huang. 2024-06-26. MammothModa: Multi-Modal Large Language Model. https://arxiv.org/abs/2406.18193

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