Mistral Small 3.1 24B Instruct 2503 Q5 K M GGUF

🚀 Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF

This project is a conversion of the Mistral model to the GGUF format, offering multilingual support and advanced vision and text capabilities.

📋 Model Information

⚠️ Important Note

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🚀 Quick Start

This model was converted to GGUF format from mistralai/Mistral-Small-3.1-24B-Instruct-2503 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

✨ Features

• Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
• Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
• Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
• Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
• Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
• Context Window: A 128k context window.
• System Prompt: Maintains strong adherence and support for system prompts.
• Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

📦 Installation

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

💻 Usage Examples

Basic Usage

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048

Advanced Usage

You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048

Model Capabilities and Use Cases

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks. This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.

Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.

It is ideal for:

• 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.

For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.