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OpenBMB released MiniCPM5-2B, a 2 billion parameter language model, currently not yet available on Hugging Face.
MiniCPM-SALA is a 9B-parameter hybrid attention model that interleaves sparse and linear attention to overcome the quadratic compute and large KV cache bottlenecks of long-context Transformers. It achieves 3.5x faster inference than Qwen3-8B at 256K tokens and supports up to 1M tokens on consumer GPUs, with a cost-effective continual training approach that reduces training costs by ~75%.
OpenBMB is hosting the Build Small Hackathon with $40k+ in prizes, focusing on building apps using small models (≤32B parameters) with Gradio on Hugging Face Spaces. Registration closes June 3, 2026.
MiniCPM5-1B is a 1B parameter model from OpenBMB that achieves impressive scores on AIME 2025 and τ2-Bench Telecom, outperforming larger models. It features both fast and reasoning modes from a single checkpoint, enabled by a three-stage post-training process including supervised fine-tuning, reinforcement learning, and on-policy distillation.
OpenBMB introduced BitCPM-CANN, a 1.58-bit model being tested on Huawei Ascend 910B hardware.