技术规格

系统要求

不同量化方法和上下文大小的显存要求

Ministral 3 3B

The Ministral 3 3B model is a compact, multimodal language model developed by Mistral AI, specifically engineered for efficient deployment in resource-constrained environments such as edge devices. It integrates a 3.4 billion parameter language model with a 0.4 billion parameter vision encoder, resulting in a total of 3.8 billion parameters. This architecture facilitates advanced capabilities including multimodal understanding, allowing the model to process and interpret both text and visual inputs. Designed to operate with a minimal memory footprint, Ministral 3 3B is capable of running locally on devices with limited VRAM, enhancing its applicability for on-device inference and privacy-sensitive applications.

Architecturally, Ministral 3 3B is a dense Transformer model that incorporates Grouped Query Attention (GQA) to optimize processing speed and memory utilization. This attention mechanism contributes to the model's ability to efficiently handle long input sequences, supporting a context length of up to 256,000 tokens. The model's design includes 8 key-value heads, which aid in capturing complex relationships within input data while maintaining computational efficiency. These technical considerations ensure a balance between performance and the practical constraints of edge computing.

The Ministral 3 3B model is suitable for a range of lightweight, real-time applications, including image captioning, text classification, real-time translation, content generation, and data extraction on edge devices. Its inherent multimodal and multilingual capabilities, supporting dozens of languages, further broaden its applicability across diverse use cases requiring local intelligence. The model also offers robust support for agentic workflows, featuring native function calling and structured JSON output, making it effective for orchestrating multi-step tasks and specialized applications.

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