README.md

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**TempestTeam**

**Mission:**  
We aim to efficiently train large-scale State Space Models (SSM) while significantly reducing infrastructure usage. Our goal is to minimize economic and environmental impacts without substantially compromising linguistic performance.

**Model:**  
**Tempest-LLM** – an efficient language model based on **Mamba2**, leveraging advanced compression methods to achieve an encoding efficiency of **1.58 bits per parameter**.

**Training Approach:**  
Our model benefits from a balanced multilingual training strategy, ensuring equal proficiency in:
- 🇫🇷 **French**
- 🇬🇧 **English**
- 🇪🇸 **Spanish**

This multilingual training enhances linguistic versatility and cultural adaptability across different languages and contexts.

**Impact:**  
- **Economic:** Reduced computational infrastructure leads to lower operational costs.
- **Ecological:** Lower power consumption and minimal infrastructure requirements decrease environmental footprint.
- **Performance:** Maintains robust linguistic accuracy and fluency despite compression and optimization.

**Vision:**  
TempestTeam is committed to showing that linguistic AI technologies can be both powerful and sustainable, contributing responsibly to AI innovation.