Mianbi AI (Facewall Intelligence) and the OpenBMB community have officially open-sourced the MiniCPM5-2B, a 2-billion-parameter text model designed for local devices such as smartphones and PCs. The model natively supports a context length of approximately 128K tokens. First previewed in July, the release now includes the full weights, training data, training recipe, and reinforcement learning (RL) system, all under the Apache 2.0 license. In independent benchmarks by Artificial Analysis, the MiniCPM5-2B scored 15 on the Intelligence Index v4.2, ranking first among all open-weight models with fewer than 4B parameters, ahead of the second-place Granite 4.2 3B (11 points). This open-source release provides developers with comprehensive resources to deploy and customize the model for on-device AI applications, demonstrating the potential of small-scale models in achieving competitive performance.
Mianbi AI (Facewall Intelligence), in collaboration with the OpenBMB community, has officially open-sourced the MiniCPM5-2B, a 2-billion-parameter text model nicknamed "Little Steel Cannon" (Xiao Gang Pao). Designed for local devices like smartphones and PCs, the model natively supports a context length of approximately 128K tokens. The model was first previewed in July, and the full release now includes the model weights, training data, training recipe, and reinforcement learning (RL) system, all distributed under the Apache 2.0 license.
In independent benchmarks conducted by Artificial Analysis, the MiniCPM5-2B achieved a score of 15 on the Intelligence Index v4.2, ranking first among all open-weight models under 4 billion parameters. It outperformed the second-place Granite 4.2 3B, which scored 11 points. The benchmark results highlight the model's efficiency and capability for small-scale, on-device AI deployments.
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