Ministral-3-3B-Base-2512-gguf

Open4bits / Ministral-3-3B-Base-2512-GGUF

This repository provides the Ministral 3 3B Base model converted to GGUF format, published by Open4bits to enable efficient local inference with reduced memory usage and broad CPU compatibility.

The underlying Ministral 3 model and architecture are developed and owned by Ministral. This repository contains only a quantized GGUF conversion of the original model weights.

The model is designed for lightweight, high-performance text generation and instruction-following tasks, making it well suited for local and resource-constrained environments.

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Model Overview

Ministral 3 is a next-generation transformer-based large language model developed for strong generalization and robust natural language understanding. This release uses the 3B parameter Base variant, optimized for general-purpose text generation, reasoning, and instruction compliance.

The GGUF format enables broad compatibility with popular local inference engines and efficient CPU-based runtimes.

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Model Details

• Architecture: Ministral 3 Base
• Parameters: ~3 billion
• Format: GGUF (quantized)
• Task: Text generation, instruction following
• Weight tying: Preserved
• Compatibility: GGUF-compatible inference runtimes (CPU-focused)

Compared to larger models in the same family, this variant offers a favorable balance of performance and resource efficiency.

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Intended Use

This model is intended for:

• Local text generation and conversational applications
• CPU-based or low-resource deployments
• Research, experimentation, and prototyping
• Self-hosted or offline AI systems

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Limitations

• Reduced performance compared to larger or non-quantized variants
• Output quality depends on prompt engineering and inference settings
• Not specifically tuned for domain-specific or specialized tasks

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License

This model is released under the original licensing terms of the base Ministral 3 model. Users must comply with the licensing conditions defined by the original model creators.

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Support

If you find this model useful, please consider supporting the project. Your support enables Open4bits to continue releasing and maintaining high-quality, efficient open models for the community.