Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network models capable of generating natural language text based on a given input or context. Trained on vast amounts of text data from diverse domains and sources, they capture the syntactic and semantic patterns inherent in natural language. LLMs have delivered impressive results across a range of natural language tasks, including text summarisation, question answering, text generation, and more.
This instructor-led, live training (available online or on-site) is designed for developers at the beginner to intermediate level who wish to leverage Large Language Models for various natural language tasks.
By the end of this training, participants will be able to:
- Set up a development environment that includes a popular LLM.
- Create a basic LLM and fine-tune it on a custom dataset.
- Apply LLMs to different natural language tasks such as text summarisation, question answering, text generation, and more.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practice opportunities.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange.
Course Outline
Introduction
- What are Large Language Models (LLMs)?
- LLMs versus traditional NLP models
- Overview of LLM 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, and more.
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 Summarisation
- Understanding the task of text summarisation and its applications
- Using an LLM for extractive and abstractive text summarisation
- Evaluating the quality of generated summaries using metrics such as ROUGE, BLEU, and more.
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 generated answers using metrics such as F1, EM, and more.
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 generated texts using parameters such as temperature, top-k, top-p, and more.
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
- Using TensorBoard to monitor and visualise the training process
- Using PyTorch Lightning to simplify training code and improve performance
- Using Hugging Face Datasets to load and preprocess data
Summary and Next Steps
Requirements
- A solid understanding of natural language processing and deep learning
- Experience with Python and PyTorch or TensorFlow
- Basic programming experience
Audience
- Developers
- NLP enthusiasts
- Data scientists
Open Training Courses require 5+ participants.
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