<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>OpenGVLab on SoloSoft</title><link>https://www.solosoft.dev/tags/opengvlab/</link><description>Recent content in OpenGVLab on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/opengvlab/index.xml" rel="self" type="application/rss+xml"/><item><title>InternVL: Open-Source Vision Language Model Family Scaling to 241B Parameters</title><link>https://www.solosoft.dev/post/internvl-vision-language-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/internvl-vision-language-2026/</guid><description>&lt;p&gt;InternVL is a series of open-source vision-language foundation models developed by &lt;a href="https://github.com/OpenGVLab"&gt;OpenGVLab&lt;/a&gt; at the Shanghai Artificial Intelligence Laboratory. The InternVL family scales vision transformers to 6 billion parameters and progressively aligns them with large language models, creating a unified architecture that achieves GPT-4o-level performance across a wide range of multimodal benchmarks. The flagship InternVL2.5-241B model represents one of the largest open-source multimodal models ever released.&lt;/p&gt;
&lt;p&gt;The project has been recognized at CVPR 2024 and has garnered significant attention for demonstrating that open-source vision-language models can match or exceed proprietary systems when scaled appropriately. InternVL&amp;rsquo;s architecture handles tasks spanning image captioning, visual question answering, document understanding, chart analysis, and multi-image reasoning, making it a versatile foundation for multimodal AI applications.&lt;/p&gt;</description></item></channel></rss>