Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network architectures capable of generating natural language texts based on provided inputs 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 delivered impressive performance across a wide array of 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 aim to leverage Large Language Models for various natural language processing tasks.
Upon completing this training, participants will be able to:
- Configure a development environment featuring a popular LLM.
- Build a basic LLM and perform fine-tuning on a custom dataset.
- Apply LLMs to diverse natural language tasks such as text summarization, question answering, and text generation.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request a customized training version of 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
- Understanding of 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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