Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Qwen2.5-3B

Qwen2.5-3B is a foundational large language model developed by Alibaba Cloud, forming a part of the broader Qwen2.5 series. This model is primarily designed for advanced natural language processing tasks, serving as a robust base model that can be further fine-tuned for specific applications. Its core purpose is to process and generate human-like text, with capabilities extended to more complex domains such as programming and mathematical problem-solving through specialized variants.

The architectural design of Qwen2.5-3B is based on the Transformer framework, integrating several key innovations for enhanced performance and efficiency. It incorporates Rotary Position Embedding (RoPE) for effective handling of sequence positions, SwiGLU as its activation function for improved non-linearity, and RMSNorm for stable normalization across layers. The model employs Grouped-Query Attention (GQA), specifically configured with 16 query heads and 2 key-value heads, which optimizes inference efficiency by reducing the memory footprint of key and value caches during sequence generation. Comprising 36 layers and a total of 3.09 billion parameters, this dense architecture is engineered for a balance of capability and computational feasibility.

Qwen2.5-3B supports a substantial context length of up to 32,768 tokens, enabling the processing of extensive textual inputs while maintaining coherence. For certain applications or instruction-tuned versions, it can support contexts up to 128,000 tokens. The model demonstrates proficiency in instruction following and the generation of structured outputs, such as JSON. It offers broad multilingual support, encompassing over 29 languages, making it suitable for global applications requiring diverse language understanding and generation capabilities. Its design focuses on providing a capable foundation for various text-based AI applications.