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Course Outline
Introduction
- What are Large Language Models (LLMs)?
- LLMs vs traditional NLP models
- Overview of LLMs features and architecture
- Challenges and limitations of LLMs
Understanding LLMs
- The lifecycle of an LLM
- How LLMs work
- The main components of an LLM: encoder, decoder, attention, 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 using an LLM
- Fine-tuning an LLM on a custom dataset
Text Summarization
- Understanding the task of text summarization and its applications
- Using an LLM for extractive and abstractive text summarization
- Evaluating the quality of the generated summaries using metrics such as ROUGE, BLEU, etc.
Question Answering
- Understanding the task of question answering and its applications
- Using an LLM for open-domain and closed-domain question answering
- Evaluating the accuracy of the generated answers using metrics such as F1, EM, etc.
Text Generation
- Understanding the task of text generation and its applications
- Using an LLM for conditional and unconditional text generation
- Controlling the style, tone, and content of the 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 the common errors and bugs in LLMs
- Using TensorBoard to monitor and visualize the training process
- Using PyTorch Lightning to simplify the training code and improve the performance
- Using Hugging Face Datasets to load and preprocess the data
Summary and Next Steps
Requirements
- An understanding of natural language processing and deep learning
- Experience with Python and PyTorch or TensorFlow
- Basic programming experience
Audience
- Developers
- NLP enthusiasts
- Data scientists
14 Hours
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