Sign up to save your podcasts
Or
Share Best Practices for Fine-Tuning Large Language Models with LoRA and QLoRA
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Best Practices for Fine-Tuning Large Language Models with LoRA and QLoRA
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.
View all episodes
By Dr. Jerry A. SmithMedium 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.