This repository serves as a curated collection of explanations for key concepts in Machine Learning, primarily focusing on advancements in Language Models. It aims to provide a structured overview of significant papers, their core contributions, and their impact on the field, targeting researchers, engineers, and students seeking to understand the evolution of ML.

How It Works

The project is structured as a comprehensive, evolving list of ML papers, categorized by topic (e.g., Language Models, Vision Transformers, Retrieval). Each entry includes the paper's title, publication date, and a concise description of its core contribution or innovation. This allows users to quickly grasp the essence of each paper and its place within the broader ML landscape.

Quick Start & Requirements

This is a reference repository; no installation or execution is required. Access is via web browser.

Highlighted Details

• Extensive coverage of major Language Model architectures (Transformer, BERT, GPT, LLaMA, Gemini, etc.) and their variants.
• Detailed sections on related ML fields including Vision Transformers, Multimodal Models, Retrieval, and Parameter-Efficient Fine-Tuning.
• Includes papers on essential ML concepts like Convolutional Neural Networks, Recurrent Layers, and Attention Mechanisms.
• Features a dedicated section on Datasets and LLM Training methodologies.

Maintenance & Community

The project is maintained by dair-ai and welcomes contributions via pull requests. A Discord server is available for community interaction and questions.

Licensing & Compatibility

The repository itself is not licensed, but it references and describes numerous open-source models and research papers, each with their own licenses. Compatibility for commercial use depends on the licenses of the individual papers and models referenced.

Limitations & Caveats

The repository is a curated list of papers and does not provide code implementations or direct access to the models themselves. The explanations are concise summaries, and deeper understanding requires consulting the original papers.