Medium Article: https://medium.com/@jsmith0475/best-practices-for-fine-tuning-large-language-models-with-lora-and-qlora-998312c82aad

Dr. Jerry A. Smith's Medium article details best practices for efficiently fine-tuning large language models (LLMs) using LoRA and QLoRA. The article emphasizes parameter efficiency to overcome challenges like memory limitations and computational costs while preventing knowledge loss. It provides a first-principles approach, outlining key strategies, including resource management, dataset quality, and training optimization. The author stresses the importance of quantization techniques and carefully considering trade-offs to achieve optimal performance. Finally, it advocates for thorough evaluation and iterative refinement for successful LLM fine-tuning.

Best Practices for Fine-Tuning Large Language Models with LoRA and QLoRA

Medium Article: https://medium.com/@jsmith0475/building-human-facing-agentic-systems-the-psychology-and-sociology-of-super-intelligence-1571cf565d51

Dr. Jerry Smith's January 2025 Medium article explores the psychological, sociological, and ethical implications of "human-facing agentic systems," AI powered by large language models (LLMs). These systems, leveraging multi-headed attention mechanisms, aim to collaborate with humans, not merely serve as tools. The article emphasizes the potential for both empowerment and manipulation inherent in these systems, highlighting the crucial role of self-interest, emotion, and identity in human-AI interaction. Smith concludes with a call for responsible development guided by transparency, inclusivity, and justice, warning against the potential for these systems to become instruments of control.

Building Human-Facing Agentic Systems: The Psychology and Sociology of Super Intelligence

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

Best Practices for Fine-Tuning Large Language Models with LoRA and QLoRA

Dr. Jerry A. Smith

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