The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. This technical report thoroughly examines the process of fine-tuning Large Language Models (LLMs), integrating theoretical insights and practical applications. It begins by tracing the historical development of LLMs, emphasising their evolution from traditional Natural Language Processing (NLP) models and their pivotal role in modern AI systems. The analysis differentiates between various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, underscoring their respective implications for specific tasks. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering the complete lifecycle from data preparation to model deployment. Key considerations include data collection strategies, handling of i

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0) Venkatesh Balavadhani Parthasarathy, Ahtsham Zafar, Aafaq Khan, and Arsalan Shahid @ CeADAR Connect Group ( August 2024 ) Abstract This technical report thoroughly examines the process of fine-tuning Large Language Models (LLMs), integrating theoretical insights and practical applications. It begins by tracing the historical development of LLMs, emphasising their evolution from traditional Natural Langua

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