技术规格

Ministral 3 3B

Ministral 3 3B is a compact, multimodal language model engineered by Mistral AI for efficient execution in edge computing environments and resource-constrained scenarios. The model architecture integrates a 3.4 billion parameter language decoder with a 410 million parameter Vision Transformer (ViT) encoder, yielding a combined capacity of approximately 3.8 billion parameters. This hybrid design enables the simultaneous processing of text and visual inputs, facilitating advanced tasks such as image captioning, visual question answering, and multimodal data extraction while maintaining a low computational overhead.

Technically, Ministral 3 3B follows a dense Transformer-based decoder-only architecture that leverages Grouped Query Attention (GQA) with 32 query heads and 8 key-value heads to optimize memory bandwidth and inference speed. It employs Rotary Positional Embeddings (RoPE) enhanced with YaRN (Yet another RoPE extensioN) and position-based softmax temperature scaling to support an extensive context window of up to 256,000 tokens. To further enhance efficiency at this scale, the 3B variant utilizes tied input-output embeddings, preventing vocabulary parameters from disproportionately increasing the total model size. The vision component utilizes a frozen ViT encoder derived from the Mistral Small 3.1 architecture, coupled with a newly trained multimodal projection layer.

The model is optimized for high-performance on-device applications, offering native support for function calling and structured JSON output to enable complex agentic workflows. It incorporates architectural refinements such as SwiGLU activation and RMSNorm to ensure stability and efficiency during local inference. By supporting dozens of languages and featuring a high-context capacity, Ministral 3 3B is positioned as a versatile solution for real-time translation, local content generation, and privacy-focused intelligent assistants operating directly on user hardware.

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