Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Qwen2.5-14B

Qwen2.5-14B is a large language model developed by the Qwen Team at Alibaba Cloud, part of the Qwen2.5 model series. It is a dense, decoder-only transformer model designed for a broad range of natural language processing tasks. The model serves as a foundational component for developers and researchers, providing a scalable base that can be further fine-tuned for specific applications. Qwen2.5-14B supports multilingual contexts, capable of understanding and generating text in over 29 languages.

The Qwen2.5-14B architecture is built upon a transformer backbone, incorporating several advanced components to enhance its capabilities. It utilizes Rotary Position Embeddings (RoPE) for effective handling of sequence length, the SwiGLU activation function for improved non-linearity, and RMSNorm for efficient layer normalization. The model employs Grouped Query Attention (GQA) with a configuration of 40 query heads and 8 key/value heads, optimizing attention mechanisms for reduced memory bandwidth during inference. Comprising 48 layers, the model is architecturally designed for computational efficiency and performance across diverse tasks.

Qwen2.5-14B is pretrained on an extensive dataset of up to 18 trillion tokens, enabling it to demonstrate proficiency in areas such as logical reasoning, coding, and mathematical tasks. The model supports an extended context window of up to 131,072 tokens, facilitating the processing of long documents and complex inputs. While the base Qwen2.5-14B model is intended for pre-training and subsequent fine-tuning, its instruction-tuned variants are optimized for direct application in conversational AI, instruction following, and generating structured outputs like JSON. Its design accommodates applications requiring significant context and precise text generation.