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, these models capture the syntactic and semantic patterns inherent in natural language. LLMs have demonstrated remarkable performance in various natural language processing tasks, including text summarization, question answering, and text generation.
This instructor-led, live training (available online or onsite) is designed for beginner to intermediate-level developers who want to leverage Large Language Models for diverse natural language tasks.
Upon completion of this training, participants will be able to:
- Configure a development environment featuring a popular LLM.
- Construct a basic 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.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
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, such as Google Colab or 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 the quality of 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 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 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 improve performance
- Using Hugging Face Datasets to load and preprocess data
Summary and Next Steps
Requirements
- Familiarity with natural language processing and deep learning concepts
- Experience with Python and either PyTorch or TensorFlow
- Basic programming proficiency
Audience
- Developers
- NLP enthusiasts
- Data scientists
Open Training Courses require 5+ participants.
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