MiniLMs: Exploring Minimal Language Model Architectures

🔍 Overview

MiniLMs is a research project focused on studying and implementing minimalist language model architectures. The project aims to understand fundamental LLM concepts by building small, efficient implementations and documenting the learning journey.

📁 Project Structure

graph TD
    A[MiniLMs Project] --> B[SYNEVA]
    A --> C[STUDY-RESOURCES]
    A --> D[Devlogs-HN]
    B --> B1[Implementation Files]
    B --> B2[Version Archive]
    C --> C1[Neural Network Basics]
    C --> C2[LLM Implementation]
    C --> C3[Research Papers]
    D --> D1[Development Logs]

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📦 Components

SYNEVA

The first practical implementation in the MiniLMs series. SYNEVA demonstrates the evolution from basic pattern matching to a markov chain with a focus on size optimization and architectural improvements, with a 3kB constraint so as to fit in a minimal QR-code sized footprint.

STUDY-RESOURCES

A curated collection of learning materials, reference implementations, and research papers used throughout the project. Includes detailed notes and practical examples.

📊 Project Goals

1. Educational

   • Understand LLM architectures from ground up
   • Document learning journey and insights
   • Create accessible examples

2. Technical

   • Implement various LLM architectures
   • Explore size vs capability trade-offs
   • Study optimization techniques

3. Research

   • Investigate minimal viable architectures
   • Document architecture transitions
   • Share findings with community

🛠️ Current Focus

• Phase 1: SYNEVA Implementation & Documentation
• Neural Network Fundamentals
• Basic Transformer Architecture
• Size Optimization Techniques

📚 Learning Path

graph LR
    A[Pattern Matching] --> B[Neural Networks]
    B --> C[Markov Chains]
    C --> D[Attention Mechanisms]
    D --> E[Transformers]
    E --> F[Advanced Architectures]

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🎯 Future Directions

1. Architecture Exploration

   • Minimal BERT implementation
   • Lightweight GPT variants
   • Custom hybrid architectures

2. Optimization Research

   • Parameter sharing techniques
   • Quantization approaches
   • Architecture pruning

3. Applications

   • Task-specific minimalist models
   • Edge device implementations
   • Browser-based demos

4. This

📝 Contributing

Contributions are welcome! Please feel free to:

• Submit implementation ideas
• Share optimization techniques
• Add study resources
• Report issues or suggest improvements

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

🔗 Related Resources

• SYNEVA Documentation
• Study Resources
• Development Logs

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MiniLMs - Understanding Language Models Through Minimal Implementations