H2O ai Large Language Models (LLMs) - Level 2

This module introduces fundamental concepts in data preparation for language models. It covers essential techniques for enhancing language model performance through proper data preparation, key functions in the data preparation process, and an introduction to LLM DataStudio as a tool for streamlining the data preparation workflow.

This comprehensive module focuses on mastering the LLM DataStudio interface and its capabilities for data curation and preparation. It provides a step-by-step guide to using LLM DataStudio for efficient data preparation, explores project management and workflow optimization, demonstrates how to prepare Q&A datasets, introduces fine-tuning principles for large language models, and covers synthetic dataset creation and model backbone selection.

This module delves into advanced techniques for fine-tuning large language models. It covers optimization strategies including Quantization and LoRA for model compression, provides insights into LLM optimization techniques, offers a comprehensive journey through H2O.ai's LLM Studio for fine-tuning, and explains the process of deploying fine-tuned LLM models with H2O LLM Studio.

This module contains the final assessment for the H2O ai Large Language Models (LLMs) - Level 2 course. The quiz evaluates learners' understanding of data preparation for language models, LLM DataStudio functionalities, and advanced fine-tuning techniques covered throughout the course.