Awesome LLM Pre-training

Introduction

The Awesome LLM Pre-training repository is a comprehensive collection of resources focused on pre-training large language models (LLMs). It is primarily aimed at developers and researchers interested in the field of natural language processing. The repository includes various frameworks, datasets, training strategies, and methods essential for pre-training LLMs.

Key Features:

• Technical Reports: A curated list of important technical papers related to LLMs.
• Training Strategies: Overview of different training frameworks, strategies, and improvements in model architecture.
• Open-source Datasets: A collection of datasets that are freely available for use in LLM training.
• Data Methods: Insights into tokenization techniques and data augmentation methods that enhance training effectiveness.

Benefits:

• Community-driven: Contributions from the open-source community ensure the repository remains relevant and up-to-date.
• Streamlined Resources: Offers a structured approach to accessing diverse resources necessary for LLM development.
• Educational Value: Helps newcomers to the field understand complex concepts and methodologies through curated content.

Highlights:

• Covers a wide range of models, including LLaMA, Baichuan, and more.
• Detailed discussions on the importance of training strategies and data quality in model performance.
• Encourages community contributions for continuous enhancement of available materials.