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Duration 14 hours
Course Outline
Introduction
- Defining Large Language Models (LLMs)
- Differentiating LLMs from conventional NLP frameworks
- Core functional attributes and architectural components of LLMs
- Operational constraints and technical limitations associated with LLMs
Foundational Concepts of LLMs
- The operational lifecycle of an LLM
- Internal mechanisms governing LLM functionality
- Primary architectural elements: encoders, decoders, attention mechanisms, and embeddings
Initial Setup and Configuration
- Establishing the foundational development environment
- Deploying LLMs as utility tools, utilizing platforms such as Google Colab or Hugging Face
Operational Management of LLMs
- Assessing the spectrum of available LLM options
- Instantiating and deploying an LLM instance
- Performing fine-tuning operations using proprietary or custom datasets
Text Summarization
- Analyzing the technical requirements and public sector applications of text summarization
- Implementing LLMs for extractive and abstractive summarization workflows
- Assessing summary fidelity using standard metrics including ROUGE and BLEU
Question Answering Systems
- Examining the technical scope and applications of automated question answering
- Applying LLMs to both open-domain and closed-domain inquiry tasks
- Validating response accuracy utilizing metrics such as F1 scores and Exact Match (EM)
Automated Text Generation
- Understanding the technical underpinnings of text generation tasks
- Implementing LLMs for both conditional and unconditional generation processes
- Regulating output style, tone, and content via parameters such as temperature, top-k, and top-p
Integration with External Frameworks and Platforms
- Integrating LLMs with PyTorch or TensorFlow frameworks
- Connecting LLMs with Flask or Streamlit application interfaces
- Deploying LLMs via cloud infrastructure providers such as Google Cloud or AWS
Diagnostic and Maintenance Procedures
- Identifying and resolving common technical errors and system bugs in LLMs
- Utilizing TensorBoard for real-time monitoring and visualization of training processes
- Leveraging PyTorch Lightning to streamline training code and optimize performance
- Employing Hugging Face Datasets for efficient data ingestion and preprocessing
Conclusions and Recommended Progression Path
Requirements
- Foundational knowledge of natural language processing and deep learning principles
- Proficiency in Python and experience with PyTorch or TensorFlow frameworks
- General programming competence
Target Audience
- Software Developers
- NLP Specialists
- Data Scientists
