| # 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. | |
| --- | |
| ## 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" | |