MiniCPM-V 4.6
Summary
MiniCPM-V 4.6 is an ultra-efficient 1.3B vision-language model optimized for mobile devices.
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@AdinaYakup: MiniCPM V4.6 a 1B MLLM that actually runs on your phone, just released by @OpenBMB 1B - Apache2.0 Runs on iOS, Android,…
OpenBMB has released MiniCPM V4.6, a 1B-parameter multimodal large language model optimized for mobile devices under the Apache 2.0 license. It features mixed visual token compression and claims approximately 1.5x faster throughput than Qwen3.5 0.8B while running natively on iOS, Android, and HarmonyOS.
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MiniCPM-V 4.5 is an 8B multimodal large language model that achieves high efficiency and strong performance through a unified 3D-Resampler architecture, a novel data strategy, and a hybrid reinforcement learning approach. The model reportedly surpasses larger proprietary and open-source benchmarks while significantly reducing GPU memory usage and inference time.
@Prince_Canuma: Congratulations to @OpenBMB on the launch of MiniCPM-V 4.6! We have Day-0 support for it on MLX-VLM h/t Magic Yang Runs…
OpenBMB has launched the MiniCPM-V 4.6 vision language model, which features immediate day-0 support on the MLX-VLM package for high-speed inference on Apple Silicon Macs.
@FeitengLi: OpenBMB open-sources MiniCPM-V 4.6, 1.3B parameters (SigLIP2-400M + Qwen3.5-0.8B), 262k context, visual encoding FLOPs 50%+ less than previous generation. Token cost for the same task is lower than Qwen3.5-0…
OpenBMB releases MiniCPM-V 4.6, a 1.3B-parameter multimodal LLM with 262k context and significantly reduced visual encoding FLOPs, achieving strong benchmark performance and broad inference framework support.