Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network architectures capable of generating natural language text based on provided input or context. Trained on extensive text data from diverse domains and sources, they capture the syntactic and semantic structures of language. LLMs have demonstrated remarkable performance across various natural language tasks, including text summarization, question answering, and text generation.
This instructor-led, live training, available online or onsite, is designed for developers with beginner to intermediate skill levels who want to leverage Large Language Models for diverse natural language applications.
Upon completing this training, participants will be able to:
- Configure a development environment featuring a popular LLM.
- Build a foundational LLM and fine-tune it using a custom dataset.
- Apply LLMs to various natural language tasks, such as text summarization, question answering, and text generation.
- Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live laboratory environment.
Customization Options
- To arrange customized training for this course, please contact us.
Course Outline
Introduction
- Definition of Large Language Models (LLMs)
- Comparison between LLMs and traditional NLP models
- Overview of LLM features and architecture
- Challenges and limitations of LLMs
Understanding LLMs
- The lifecycle of an LLM
- How LLMs operate
- Core 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 utilizing 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 summary quality 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 answer accuracy 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 generated texts via 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
- Using TensorBoard to monitor and visualize the training process
- Using PyTorch Lightning to simplify training code and enhance performance
- Using Hugging Face Datasets to load and preprocess data
Summary and Next Steps
Requirements
- Knowledge of natural language processing and deep learning
- Experience with Python and PyTorch or TensorFlow
- Basic programming proficiency
Audience
- Developers
- NLP enthusiasts
- Data scientists
Open Training Courses require 5+ participants.
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