Valley is a cutting-edge multimodal large model designed to handle a variety of tasks involving text, images, and video data, which is developed by ByteDance. Our model
Achieved the best results in the inhouse e-commerce and short-video benchmarks, much better then other SOTA opensource models.
Demonstrated comparatively outstanding performance in the OpenCompass Benchmark.
Release
[2025/10/26] 🔥🔥🔥 Update Valley3, significantly enhance multimodal understanding and reasoning capabilities, achieving 74.4 on OpenCompass Multi-modal Academic Leaderboard!
[2025/02/15] 🔥 Update Valley2-DPO, achieve 69.6 on OpenCompass Multi-modal Academic Leaderboard and update AutoModel usage for checkpoints.
The foundational version of Valley2 is a multimodal large model aligned with Siglip and Qwen2.5, incorporating LargeMLP and ConvAdapter to construct the projector.
In the final version, we also referenced Eagle, introducing an additional VisionEncoder that can flexibly adjust the number of tokens and is parallelized with the original visual tokens.
This enhancement supplements the model’s performance in extreme scenarios, and we chose the Qwen2vl VisionEncoder for this purpose.
All of our open-source models are licensed under the Apache-2.0 license.
We are Hiring
The Data-Ecommerce-Platform Governance-Basic Algorithms Team focuses on the research and development of multi-modal large model algorithms and foundational algorithms, continuously delving deeply into this field. Our mission is to optimize algorithms and collaborate with business teams to comprehensively govern the quality and ecosystem of ByteDance's e-commerce products. Currently, the team has a strong demand for foundational algorithm expertise in NLP, CV, and multimodal technologies. We welcome inquiries and look forward to working on challenging projects with talented individuals like you!
@article{wu2025valley2,
title={Valley2: Exploring Multimodal Models with Scalable Vision-Language Design},
author={Wu, Ziheng and Chen, Zhenghao and Luo, Ruipu and Zhang, Can and Gao, Yuan and He, Zhentao and Wang, Xian and Lin, Haoran and Qiu, Minghui},
journal={arXiv preprint arXiv:2501.05901},
year={2025}
}
