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arXiv · 2501.05901

Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Ziheng Wu·Zhenghao Chen·Ruipu Luo·Can Zhang·Yuan Gao·Zhentao He·Xian Wang·Haoran Lin·Minghui Qiu
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Abstract

Recently, vision-language models have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introduce Valley2, a novel multimodal large language model designed to enhance performance across all domains and extend the boundaries of practical applications in e-commerce and short video scenarios. Notably, Valley2 achieves state-of-the-art (SOTA) performance on e-commerce benchmarks, surpassing open-source models of similar size by a large margin (79.66 vs. 72.76). Additionally, Valley2 ranks second on the OpenCompass leaderboard among models with fewer than 10B parameters, with an impressive average score of 67.4. The code and model weights are open-sourced at https://github.com/bytedance/Valley.

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Ziheng Wu, Zhenghao Chen, Ruipu Luo, Can Zhang, Yuan Gao, Zhentao He, Xian Wang, Haoran Lin, Minghui Qiu. 2025-01-10. Valley2: Exploring Multimodal Models with Scalable Vision-Language Design. https://arxiv.org/abs/2501.05901

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