MiniCPM is an open-source family of compact large language models developed by OpenBMB for efficient AI inference on personal devices and resource-constrained hardware. Its latest MiniCPM5 series includes 1B and 2B-class models optimized for local deployment. The models support text generation, coding, mathematical reasoning, and autonomous agent workflows. MiniCPM5-2B uses a Transformer architecture with grouped-query attention and a 131,072-token context window. Earlier releases explore sparse attention, mixture-of-experts architectures, and accelerated inference. Developers can deploy models through Hugging Face Transformers, llama.cpp, and Apple MLX using compatible checkpoints. The repository also provides training resources, fine-tuning tools, quantized models, and deployment guides.
Features
- Compact 1B and 2B-Class Language Models
- On-Device and Resource-Efficient AI Inference
- 131,072-Token Long-Context Processing
- Coding, Mathematical Reasoning, and Tool Use
- Quantized GGUF and Apple MLX Model Support
- Fine-Tuning, Deployment, and Training Resources