Mistral 7B Explained: Towards More Efficient Language Models

Mistral 7B is a powerful and efficient large language model developed by Mistral AI, a Paris-based startup founded by former Meta and Google DeepMind employees. The model uses a decoder-only architecture, which is common in models designed for natural language generation tasks. It is available in both base and instruct forms, with the base version being similar to chat variants and suitable for both conversational and instruction-based tasks. Mistral 7B's performance is strong compared to larger models, outperforming Llama 2 13B and matching or exceeding Llama 1 34B in most benchmarks. The model's efficiency is achieved through advancements in transformer architectures, including Root Mean Square Normalization (RMS Norm), Rotary Position Embedding (RoPE), Grouped Query Attention (GQA), Sliding Window Attention (SWA), Rolling Buffer KV Cache, and the SwiGLU activation function. These components will be explored in detail in the following sections.