To install this model locally in the shortest time, opt for Docker.
Follow the step-by-step instructions below.
The setup auto-downloads all needed files (several GBs).
You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.
The **MiniMax-M2.7** model sets a new benchmark for efficiency in large language models, delivering exceptional performance with a compact footprint. It features a **parameter count** of 7.7 billion, enabling fast inference on standard hardware while maintaining high accuracy across diverse tasks. The architecture incorporates advanced **attention mechanisms** and a novel quantization scheme that reduces memory usage without sacrificing model depth. In benchmark evaluations, MiniMax-M2.7 achieves state-of-the-art results in natural language understanding, coding, and multilingual generation, outperforming previous models in the same size class. Its integration with the **MiniMax ecosystem** provides developers seamless access to optimized APIs, fine‑tuning tools, and safety filters, ensuring reliable deployment in production environments. The model’s **open-source** release encourages community contributions, fostering rapid iteration and the development of new applications built on its robust foundation.
| Spec | Value |
|---|---|
| Parameter Count | 7.7B |
| Context Length | 8K tokens |
| Training Data | 2.5T tokens (web + code) |
| Inference Speed | >200 tokens/s (GPU) |
- Vulkan API translation layer patch for boosting frames on Linux systems
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- Overlay display disabler patch for reclaiming wasted graphics memory
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- Standalone trainer executable generator utilizing compiled cheat sheets
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- Raw mouse input enabler patch removing forced camera smoothing acceleration
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