Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Mistral-Small-2501

Mistral Small 3, specifically the Mistral-Small-2501 variant, is a 24-billion-parameter language model developed by Mistral AI, engineered for optimal efficiency and low-latency performance in generative AI tasks. This model is delivered as both a pre-trained base model and an instruction-tuned checkpoint, making it suitable for a range of language-centric applications. Its release under the Apache 2.0 license underscores its commitment to an open ecosystem, enabling widespread adoption and modification.

The architectural foundation of Mistral-Small-2501 is a dense transformer network, distinguished by a design that incorporates fewer layers compared to larger models, thereby minimizing time per forward pass. The model utilizes Grouped-Query Attention (GQA) to enhance inference efficiency and integrates Rotary Position Embeddings (RoPE) for effective positional encoding. The SwiGLU activation function is employed within its layers. With a substantial context window of 32,768 tokens, the model is capable of processing and generating extended sequences of text. It supports multiple languages, reinforcing its applicability in diverse global contexts.

Mistral Small 3 (Mistral-Small-2501) is designed for practical deployment, emphasizing rapid response times. It exhibits performance characteristics that position it as a proficient solution for scenarios demanding quick and accurate language processing, such as conversational agents, automated function calling, and specialized domain-specific applications through fine-tuning. Its efficient architecture allows for deployment on various computational platforms, including consumer-grade hardware, making it suitable for localized inference and applications with strict latency requirements.