Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are advanced deep neural network models designed to generate natural language texts based on given inputs or contexts. These models are trained on extensive text data from diverse domains and sources, enabling them to capture both the syntactic and semantic patterns of natural language. LLMs have demonstrated significant achievements in various natural language tasks, including text summarization, question answering, text generation, and more.
This instructor-led, live training (online or onsite) is designed for beginner to intermediate level developers who wish to utilize Large Language Models for a variety of natural language tasks relevant to government operations.
By the end of this training, participants will be able to:
- Set up a development environment that includes a popular LLM.
- Create and fine-tune a basic LLM on a custom dataset specific to governmental needs.
- Apply LLMs to different natural language tasks such as text summarization, question answering, text generation, and more, tailored for government applications.
- Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
**Format of the Course**
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
**Course Customization Options**
To request a customized training for government-specific requirements, please contact us to arrange.This course is available as onsite live training in US Government or online live training.
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Course Outline
Introduction
- What are Large Language Models (LLMs)?
- Comparison of LLMs with traditional Natural Language Processing (NLP) models
- Overview of LLM features and architecture
- Challenges and limitations associated with LLMs
Understanding LLMs
- The lifecycle of an LLM, from development to deployment
- Detailed explanation of how LLMs function
- Key components of an LLM: encoder, decoder, attention mechanisms, embeddings, and more
Getting Started
- Setting up the development environment for government use
- Installing an LLM as a development tool, such as Google Colab or Hugging Face, for government applications
Working with LLMs
- Exploring available LLM options suitable for government tasks
- Creating and utilizing an LLM for government projects
- Fine-tuning an LLM on a custom dataset specific to government needs
Text Summarization
- Understanding the task of text summarization and its applications in government operations
- Using an LLM for both extractive and abstractive text summarization for government documents
- Evaluating the quality of generated summaries using metrics such as ROUGE, BLEU, etc., for government reporting
Question Answering
- Understanding the task of question answering and its applications in public sector information retrieval
- Using an LLM for open-domain and closed-domain question answering for government inquiries
- Evaluating the accuracy of generated answers using metrics such as F1, Exact Match (EM), etc., for government assessments
Text Generation
- Understanding the task of text generation and its applications in government communications
- Using an LLM for conditional and unconditional text generation for government reports and documents
- Controlling the style, tone, and content of generated texts using parameters such as temperature, top-k, top-p, etc., to meet government standards
Integrating LLMs with Other Frameworks and Platforms
- Using LLMs with PyTorch or TensorFlow for government projects
- Using LLMs with Flask or Streamlit to develop government applications
- Using LLMs with Google Cloud or AWS for scalable government solutions
Troubleshooting
- Understanding common errors and bugs in LLMs for government use cases
- Using TensorBoard to monitor and visualize the training process for government models
- Using PyTorch Lightning to simplify the training code and improve performance for government applications
- Using Hugging Face Datasets to load and preprocess data for government projects
Summary and Next Steps
Requirements
- An understanding of natural language processing and deep learning for government applications.
- Experience with Python and PyTorch or TensorFlow.
- Basic programming experience.
Audience
- Government developers
- NLP enthusiasts in the public sector
- Data scientists for government agencies
14 Hours
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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