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Duration 14 hours
Course Outline
Introduction
- Defining Large Language Models (LLMs)
- Comparing LLMs with traditional NLP models
- Key features and architectural overview of LLMs
- Identifying the challenges and limitations of LLMs
Concepts Behind LLMs
- The complete lifecycle of an LLM
- Mechanisms of how LLMs operate
- Core components including encoder, decoder, attention, and embeddings
Initial Setup
- Preparing the development environment
- Installing an LLM via development tools such as Google Colab or Hugging Face
Working with LLMs
- Reviewing available LLM options
- Developing and deploying an LLM
- Fine-tuning an LLM using a custom dataset
Text Summarization
- Exploring the applications and mechanics of text summarization
- Applying LLMs for both extractive and abstractive summarization
- Assessing summary quality using metrics like ROUGE and BLEU
Question Answering
- Understanding the scope and applications of question answering
- Using LLMs for open-domain and closed-domain querying
- Evaluating answer accuracy with metrics such as F1 and EM
Text Generation
- Exploring the role of text generation in various applications
- Implementing conditional and unconditional text generation with LLMs
- Managing style, tone, and content via parameters like temperature, top-k, and top-p
Integrations with Frameworks and Platforms
- Integrating LLMs with PyTorch or TensorFlow
- Building interfaces using Flask or Streamlit
- Deploying LLMs on Google Cloud or AWS
Troubleshooting and Optimization
- Identifying common errors and bugs in LLM development
- Monitoring and visualizing training processes with TensorBoard
- Simplifying training code and enhancing performance using PyTorch Lightning
- Loading and preprocessing data efficiently with Hugging Face Datasets
Conclusion and Future Pathways
Requirements
- Familiarity with natural language processing and deep learning concepts
- Practical experience with Python and either PyTorch or TensorFlow
- Fundamental programming skills
Who Should Attend
- Software developers
- NLP enthusiasts
- Data scientists
