Mistral Small3.1 24B Instruct - Details

Mistral Small3.1 24B Instruct is a large language model developed by Mistral Ai, a company specializing in advanced AI technologies. With 24B parameters, it offers robust capabilities for complex tasks. The model is released under the Apache License 2.0 (Apache-2.0), ensuring open access and flexibility. Designed for instruction-following, it features an expanded context window up to 128k tokens, enhancing its ability to handle lengthy and detailed interactions.

Mistral-Small-3.1-24B-Instruct-2503 is an instruction-finetuned large language model with 24 billion parameters, designed for advanced tasks. It includes state-of-the-art vision understanding and enhanced long context capabilities up to 128k tokens, making it suitable for handling complex, extended interactions. The model supports multilingual tasks and offers agentic capabilities with native function calling, enabling dynamic and interactive applications. Optimized for local deployment on devices like RTX 4090 or 32GB RAM MacBooks, it excels in conversational agents, function calling, fine-tuning, sensitive data inference, programming/math reasoning, document understanding, and visual analysis.

Mistral-Small-3.1-24B-Instruct-2503 is a large language model with 24b parameters, placing it in the category of powerful tools for complex tasks, though it requires significant computational resources. Its 128k context length enables handling extensive texts and detailed interactions, making it ideal for applications like document analysis or long-form reasoning, but this capability also demands higher memory and processing power. The model’s design balances advanced performance with practical deployment options, catering to both sophisticated tasks and local execution environments.

• Parameter Size: 24b
• Context Length: 128k

Mistral-Small-3.1-24B-Instruct-2503 is a versatile large language model with possible applications in areas like fast-response conversational agents, low-latency function calling, and subject matter expertise through fine-tuning. Its possible uses could include local inference for users prioritizing data privacy, programming and math reasoning tasks, or analyzing long documents and visual content. The model’s multilingual support, covering languages like Spanish, Chinese, and Arabic, suggests possible value for projects requiring cross-lingual capabilities. However, these possible uses require thorough evaluation to ensure alignment with specific needs and constraints. The model’s design emphasizes flexibility, but its effectiveness in any given scenario depends on careful testing and adaptation.

• fast-response conversational agents
• low-latency function calling
• subject matter experts via fine-tuning
• local inference for hobbyists and organizations handling sensitive data
• programming and math reasoning
• long document understanding
• visual understanding

Mistral-Small-3.1-24B-Instruct-2503 is a large language model with possible applications in areas like fast-response conversational agents, low-latency function calling, and subject matter expertise through fine-tuning. Its possible uses could include local inference for users prioritizing data privacy or handling sensitive information, as well as programming and math reasoning tasks. The model’s possible value extends to long document understanding and visual analysis, though these possible applications require careful validation. The model’s multilingual support and context length make it possible to explore cross-lingual or complex reasoning tasks, but each possible use must be thoroughly evaluated and tested before deployment.

• fast-response conversational agents
• low-latency function calling
• subject matter experts via fine-tuning
• local inference for sensitive data scenarios

Mistral-Small-3.1-24B-Instruct-2503’s medium q4 version requires a GPU with at least 24GB VRAM (e.g., RTX 3090 Ti, A100) and 32GB system memory for optimal performance, balancing precision and efficiency. This possible configuration allows deployment on devices like RTX 4090 or 32GB RAM MacBooks, though users should verify compatibility. The q4 quantization reduces resource demands compared to fp16 while maintaining reasonable accuracy, making it a possible choice for local inference.

• fp16, q4, q8

Mistral-Small-3.1-24B-Instruct-2503 is a large language model with 24 billion parameters and an expanded context window up to 128k tokens, designed for complex tasks like conversational agents, function calling, and document analysis while supporting multilingual capabilities across 30+ languages. Its quantized versions (fp16, q4, q8) enable flexible deployment on devices with varying hardware, making it suitable for local inference and sensitive data scenarios.

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