MiniCPM-V 4.7 发布：35B 多模态 MoE 模型仅用 3GB 内存跑在手机上
OpenBMB 把 35B 的多模态模型塞进了 3GB 内存，手机能离线读文档、认路牌，还给了 1B 小版本和微调方法，端侧玩法直接变多了。
OpenBMB 发布 MiniCPM-V 4.7，主体是 35B-A3B 的 MoE 多模态模型，通过 LES（Lookahead Expert Scheduling）在手机上以约 3GB 内存本地运行。模型可读路牌、解析复杂文档并对视觉数据做推理，同时提供面向低配硬件的 1B 轻量版本。开发者可用 Self-Replay Fine-Tuning（SRFT）针对新任务微调，且不损失通用性能，支持 iOS 和 Android 本地部署。
🎬 35B MoE multimodal AI. On your phone. With just 3GB of memory. 🤯
This is MiniCPM-V 4.7 in action: reading real-world signs, planning around constraints, parsing complex documents, and reasoning over visual data.
Powered by LES (Lookahead Expert Scheduling), the 35B-A3B model brings efficient MoE inference to mobile devices, while the lightweight 1B variant delivers capable multimodal understanding on resource-constrained hardware.
🛠️ With Self-Replay Fine-Tuning (SRFT), developers can customize models for new tasks while maintaining general performance.
From iOS to Android, MiniCPM-V 4.7 makes powerful multimodal AI more practical to run locally.
Try it 👇 🔗 https://t.co/msjm2QlrvX 🤗 https://t.co/W95QKGhymw Self-Replay Fine-Tuning: https://t.co/asg1reRCtW
