https://github.com/kuberwastaken/minilms
a research project focused on studying and implementing minimalist language model architectures.
https://github.com/kuberwastaken/minilms
artificial-intelligence llm machine-learning minification minilms research
Last synced: about 1 year ago
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a research project focused on studying and implementing minimalist language model architectures.
- Host: GitHub
- URL: https://github.com/kuberwastaken/minilms
- Owner: Kuberwastaken
- License: apache-2.0
- Created: 2025-04-19T07:12:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-18T18:31:22.000Z (about 1 year ago)
- Last Synced: 2025-05-18T19:33:39.532Z (about 1 year ago)
- Topics: artificial-intelligence, llm, machine-learning, minification, minilms, research
- Language: Jupyter Notebook
- Homepage: http://minilms.kuber.studio/
- Size: 21.2 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
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
```mermaid
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]
```
## 📦 Components
### [SYNEVA](./SYNEVA/README.md)
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](./STUDY-RESOURCES/README.md)
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
```mermaid
graph LR
A[Pattern Matching] --> B[Neural Networks]
B --> C[Markov Chains]
C --> D[Attention Mechanisms]
D --> E[Transformers]
E --> F[Advanced Architectures]
```
## 🎯 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](LICENSE) file for details.
## 🔗 Related Resources
- [SYNEVA Documentation](./SYNEVA/README.md)
- [Study Resources](./STUDY-RESOURCES/README.md)
- [Development Logs](./DEVLOGS/)
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MiniLMs - Understanding Language Models Through Minimal Implementations