Qwen2.5 is a series of large language models (LLMs) with significant improvements over previous models, focusing on efficiency, performance, and long sequence handling. Key architectural advancements include Grouped Query Attention (GQA) for better memory management, Mixture-of-Experts (MoE) for enhanced capacity, and Rotary Positional Embeddings (RoPE) for effective long-sequence modeling. Qwen2.5 uses two-phase pre-training and progressive context length expansion to enhance long-context capabilities, along with techniques like YARN, Dual Chunk Attention (DCA), and sparse attention. It also features an expanded tokenizer and uses SwiGLU activation, QKV bias and RMSNorm for stable training.

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