OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device

OpenBMB released MiniCPM5-2B, a 2.52B-parameter dense causal language model with a native 131,072-token context, averaging 53.9 across 34 benchmarks and outperforming Qwen3.5-4B's 51.1, with notable strengths in tool use, coding agents, and long-context retrieval. The model, built to run on device with GGUF builds starting at 1.56 GB, is available under Apache 2.0 along with training datasets and intermediate checkpoints, and supports vLLM, SGLang, llama.cpp, Ollama, and MLX without code forks.

OpenBMB has released MiniCPM5-2B, a dense causal language model with 2,516,756,480 parameters and a native 131,072 token context. It averages 53.9 across the 34 benchmarks in its model card, ahead of Qwen3.5-4B at 51.1, with its clearest leads in tool use, coding agents and long-context retrieval. Post-training pairs 400B tokens of deep-thinking SFT with RL teachers and on-policy distillation that merges 16 expert models into one checkpoint. The weights ship under Apache 2.0 alongside the pre-training, SFT and RL datasets and the intermediate Base, Midtrain and SFT-only checkpoints. GGUF builds start at 1.56 GB, and the standard LlamaForCausalLM architecture loads in vLLM, SGLang, llama.cpp, Ollama and MLX without a model-code fork. The post OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device https://www.marktechpost.com/2026/09/07/openbmb-releases-minicpm5-2b-a-2-52b-dense-model-averaging-53-9-across-34-benchmarks-and-built-to-run-on-device/ appeared first on MarkTechPost https://www.marktechpost.com .