Tutorials on Fine Tuning Large Language Models

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Fine-Tuning Llama 3 on a Single GPU: Memory vs Accuracy Tradeoffs

The short version: You hit the same wall every time: a fine-tuning job that should work crashes with an out-of-memory error, or accuracy drops because the only config that fits uses aggressive memory cuts. The memory-vs-accuracy question determines whether your single-GPU workflow succeeds, or…
Thumbnail Image of Tutorial Fine-Tuning Llama 3 on a Single GPU: Memory vs Accuracy Tradeoffs
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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How to Fine-Tune LLMs: LoRA vs Full Fine-Tuning Trade-offs

You can't pick how to fine-tune an LLM based on vague rules of thumb. The decision hinges on compute limits, task type, and how much accuracy you're willing to trade. Full fine-tuning updates every parameter in the model, giving you maximum task performance but requiring substantial GPU memory for…
Thumbnail Image of Tutorial How to Fine-Tune LLMs: LoRA vs Full Fine-Tuning Trade-offs
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Fine-Tune LLMs on a Single GPU Using LoRA and PEFT

LoRA makes single-GPU fine-tuning possible by updating adapters instead of rewriting the full model. QLoRA pushes further by quantizing the frozen base model, which frees up memory for the trainable adapter, optimizer state, and batches. Here's what single-GPU LoRA and QLoRA workflows look like in…
Thumbnail Image of Tutorial Fine-Tune LLMs on a Single GPU Using LoRA and PEFT
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
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