# MALM: Modular Adapter-based Language Model  
📄 [Read the full paper (MALM.pdf)](./MALM.pdf)  
📝 Author: **Hilal Limo (Independent Researcher, 15)**  
📜 License: [Apache-2.0](./LICENSE)  

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## Overview
This repository contains the research paper **MALM: Modular Adapter-based Language Model**, which introduces a lightweight and scalable framework for multilingual AI.  
Instead of relying on massive monolithic models, MALM separates **reasoning** and **translation** into two modular parts:

- **Core Language Model (CLM):** A compact, English-focused reasoning engine.  
- **Specialized Translation Adapters (STAs):** Lightweight, swappable neural machine translation models.  
- **Orchestration Layer:** Connects the pieces, parsing delegation tokens (e.g. `<to:de> ... </to>`) and routing requests to the right adapter.  

This design drastically reduces compute cost, makes it easier to add new languages, and is especially useful for **small models**, edge devices, and research settings.

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## Why MALM?
- 🚀 **Efficiency:** Keep one reasoning core small and sharp.  
- 🌍 **Scalability:** Add or update languages by swapping STAs.  
- 🛠️ **Maintainability:** Upgrade individual adapters without retraining the whole system.  
- 📱 **Small Models:** Perfect for low-resource environments, edge devices, and startups.  

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## Example Conversation Flows
```text
User: Translate "my name is Adam" into German.
CLM → <to:de> my name is Adam </to>
STA → "Mein Name ist Adam"

User (in Spanish): "¿Cuánto es 12 + 7?"
Input STA (es→en) → "How much is 12 + 7?"
CLM → "The answer is <to:es> 19 </to>"
Output STA → "La respuesta es 19"