Pretraining LLMs

What You'll Learn

• Understand all steps for pretraining an LLM, from data preparation to model performance assessment.
• Explore model configuration options, including modifying architectures and weight initialization.
• Learn innovative techniques like Depth Upscaling to reduce training costs significantly.

About This Course

This course delves into the initial step of training large language models through pretraining, covering essential steps, cost considerations, and effective strategies for using existing open-source models to reduce expenses.

• Optimal Scenarios: Understand when pretraining is the best choice for enhancing model performance.
• Dataset Creation: Learn to build a high-quality training dataset using web text and existing resources.
• Data Preparation: Prepare your dataset for training, formatted for use with the Hugging Face library.
• Model Configuration: Configure model initialization and explore choices impacting pretraining speed.
• Execution: Set up and run a training process, guiding you through the actual training process.
• Performance Evaluation: Assess trained models using benchmark tasks and standard evaluation strategies.

Course Outline

• Introduction
  Overview of the course, covering the goals and structure of LLM pretraining.
• Why Pre-training
  Understanding the purpose and benefits of pretraining for language models.
• Data Preparation
  Techniques for preparing datasets to ensure quality training data for LLMs.
• Packaging Data for Pretraining
  Formatting data for use in the pretraining process with the Hugging Face library.
• Model Initialization
  Configuring model initialization options to optimize the pretraining process.
• Training in Action
  Practical application of training, including setup and execution details.
• Evaluation
  Methods and metrics for evaluating pre-trained LLM performance.
• Conclusion
  Summary of key takeaways and next steps for implementing pretraining in projects.

Who Should Join?

This course is ideal for AI enthusiasts, data scientists, and ML engineers seeking to understand pretraining for LLMs. Basic Python and LLM knowledge are recommended.