[2408.13296] The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

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Computer Science > Machine Learning

arXiv:2408.13296 (cs)

[Submitted on 23 Aug 2024 (v1), last revised 30 Oct 2024 (this version, v3)]

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

Authors:Venkatesh Balavadhani Parthasarathy, Ahtsham Zafar, Aafaq Khan, Arsalan Shahid<br>View a PDF of the paper titled The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities, by Venkatesh Balavadhani Parthasarathy and 3 other authors

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Abstract:This report examines the fine-tuning of Large Language Models (LLMs), integrating theoretical insights with practical applications. It outlines the historical evolution of LLMs from traditional Natural Language Processing (NLP) models to their pivotal role in AI. A comparison of fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, highlights their applicability to different tasks. The report introduces a structured seven-stage pipeline for fine-tuning LLMs, spanning data preparation, model initialization, hyperparameter tuning, and model deployment. Emphasis is placed on managing imbalanced datasets and optimization techniques. Parameter-efficient methods like Low-Rank Adaptation (LoRA) and Half Fine-Tuning are explored for balancing computational efficiency with performance. Advanced techniques such as memory fine-tuning, Mixture of Experts (MoE), and Mixture of Agents (MoA) are discussed for leveraging specialized networks and multi-agent collaboration. The report also examines novel approaches like Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), which align LLMs with human preferences, alongside pruning and routing optimizations to improve efficiency. Further sections cover validation frameworks, post-deployment monitoring, and inference optimization, with attention to deploying LLMs on distributed and cloud-based platforms. Emerging areas such as multimodal LLMs, fine-tuning for audio and speech, and challenges related to scalability, privacy, and accountability are also addressed. This report offers actionable insights for researchers and practitioners navigating LLM fine-tuning in an evolving landscape.

Subjects:

Machine Learning (cs.LG); Computation and Language (cs.CL)

Cite as:<br>arXiv:2408.13296 [cs.LG]

(or<br>arXiv:2408.13296v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2408.13296

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arXiv-issued DOI via DataCite

Submission history<br>From: Arsalan Shahid [view email]<br>[v1]<br>Fri, 23 Aug 2024 14:48:02 UTC (13,396 KB)

[v2]<br>Mon, 21 Oct 2024 11:10:00 UTC (13,398 KB)

[v3]<br>Wed, 30 Oct 2024 01:04:15 UTC (11,870 KB)

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View a PDF of the paper titled The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities, by Venkatesh Balavadhani Parthasarathy and 3 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

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