Summary of 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 et al.
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, Ahtsham Zafar, Aafaq Khan, Arsalan Shahid
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A medium-difficulty summary of this report on Large Language Models (LLMs) fine-tuning explores the integration of theoretical insights with practical applications. The paper compares fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, highlighting their suitability for various tasks. A structured seven-stage pipeline for fine-tuning LLMs is introduced, covering data preparation, model initialization, hyperparameter tuning, and model deployment. Emphasis is placed on managing imbalanced datasets and optimization techniques like Low-Rank Adaptation (LoRA) and Half Fine-Tuning to balance 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. Novel approaches like Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO) align LLMs with human preferences. The report also covers validation frameworks, post-deployment monitoring, and inference optimization, focusing on 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 addressed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores the fine-tuning of Large Language Models (LLMs) and how they can be used in various applications. It looks at different ways to fine-tune LLMs and introduces a seven-stage process for doing so. The report also discusses ways to manage datasets and make sure the models are efficient. |
Keywords
» Artificial intelligence » Fine tuning » Hyperparameter » Inference » Lora » Low rank adaptation » Mixture of experts » Optimization » Supervised » Unsupervised
