Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use 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.
- Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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