Computer Science > Computer Vision and Pattern Recognition
[Submitted on 8 Feb 2024 (v1), last revised 26 Jun 2024 (this version, v2)]
Title:SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
View PDF HTML (experimental)Abstract:We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX framework by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm. To fully unleash the potential of MLLMs, we assemble a comprehensive multi-domain and multimodal dataset covering publicly available resources in language, vision, and vision-language tasks. We further enrich this collection with our curated OCR intensive and Set-of-Mark datasets, extending the diversity and generality. By training over different base LLMs including TinyLlama1.1B, InternLM2-7B, LLaMA2-13B, and Mixtral8x7B, we obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities. Comprehensive benchmarking reveals a strong correlation between the multi-modal performance with the data and parameter scales. Code and models are released at this https URL
Submission history
From: Renrui Zhang [view email][v1] Thu, 8 Feb 2024 18:59:48 UTC (12,924 KB)
[v2] Wed, 26 Jun 2024 07:59:03 UTC (12,945 KB)
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