MiniCPM3-4B is the third-generation MiniCPM language model, offering efficient and accurate multi-task capabilities with 4B parameters.
Technical highlights
：Using an efficient decoder-only Transformer architecture, the number of attention heads and feedforward network layer dimensions are carefully designed and tuned. This optimization enables the model to still have powerful task processing capabilities despite being lightweight with only 4 billion parameters, and has demonstrated competitiveness in multiple benchmark tests with models with far more parameters than itself.
：With the help of distributed training frameworks such as DeepSpeed and Megatron-LM, the training process can be deployed in parallel across multiple GPUs and multiple computing nodes to accelerate convergence and reduce resource consumption; dynamic loss scaling and gradient checkpoint technology are used to prevent numerical overflow and reduce video memory usage. The training data construction phase uses intelligent filtering and deduplication mechanisms to ensure that the model is free from interference from text samples of low quality, low diversity, and low information value.
：Adopt a new word segmentation scheme based on Byte-Pair Encoding (BPE) and optimized for multiple languages (especially Chinese and English); use LoRA, prefix fine-tuning and other parameter efficient fine-tuning methods for specific task variants to achieve rapid adaptation with minimal parameter adjustments; the reasoning level integrates mechanisms such as quantized perception training and attention caching to further improve reasoning efficiency and deployment flexibility.