Recently, Alibaba Damo Academy launched a multi-modal large language model named Valley 2, designed for e-commerce scenarios. It aims to enhance performance across various fields and expand the application boundaries of e-commerce and short video scenarios through a scalable vision-language architecture. Valley 2 utilizes Qwen 2.5 as its LLM backbone, paired with the SigLIP-384 visual encoder, incorporating MLP layers and convolution for efficient feature transformation.

Recently, Alibaba DAMO Academy (Hangzhou) Technology Co., Ltd.'s model open-source community, ModelScope, announced a significant breakthrough: the release of the InternVL 2.5 model. This open-source multimodal large language model has achieved exceptional performance, becoming the first open-source model to exceed 70% accuracy on the Multimodal Understanding Benchmark (MMMU), rivaling commercial models like GPT-4o and Claude-3.5-Sonnet.

Zhipu Technology recently announced the open source of its end-side large language and multimodal model GLM-Edge series, marking an important attempt by the company in real-world use cases at the end side. The GLM-Edge series consists of four different model sizes, including GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, and GLM-Edge-V-5B, which are optimized for mobile platforms such as smartphones and vehicle systems, as well as desktop platforms like PCs.