Introduction to Large Language Models (LLMs) Training Course

Introduction

• What are Large Language Models (LLMs)?
• LLMs compared to traditional NLP models.
• Overview of LLM features and architecture.
• Challenges and limitations of LLMs.

Understanding LLMs

• The lifecycle of an LLM.
• How LLMs function.
• Key components of an LLM: encoder, decoder, attention mechanisms, embeddings, etc.

Getting Started

• Setting up the development environment.
• Installing an LLM as a development tool, e.g., Google Colab, Hugging Face.

Working with LLMs

• Exploring available LLM options.
• Creating and deploying an LLM.
• Fine-tuning an LLM on a custom dataset.

Text Summarization

• Understanding text summarization tasks and their applications.
• Utilizing an LLM for extractive and abstractive text summarization.
• Evaluating summary quality using metrics such as ROUGE, BLEU, etc.

Question Answering

• Understanding question answering tasks and their applications.
• Utilizing an LLM for open-domain and closed-domain question answering.
• Evaluating answer accuracy using metrics such as F1, EM, etc.

Text Generation

• Understanding text generation tasks and their applications.
• Utilizing an LLM for conditional and unconditional text generation.
• Controlling the style, tone, and content of generated texts using parameters such as temperature, top-k, top-p, etc.

Integrating LLMs with Other Frameworks and Platforms

• Using LLMs with PyTorch or TensorFlow.
• Using LLMs with Flask or Streamlit.
• Using LLMs with Google Cloud or AWS.

Troubleshooting

• Understanding common errors and bugs in LLMs.
• Monitoring and visualizing the training process using TensorBoard.
• Simplifying training code and improving performance with PyTorch Lightning.
• Loading and preprocessing data using Hugging Face Datasets.

Summary and Next Steps