Tutorials on Lora Fine Tuning

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NEW

Fine-Tuning LLMs: When to Use SFT, RLHF, or DPO and How Quantization Affects Memory

Before we get into it, the short version: Two tables below show memory, quality, and hardware trade-offs across fine-tuning methods. First table covers general characteristics. Summary blocks after it explain when you'd pick each approach. Memory summary: Full fine-tuning updates every weight, so…
Thumbnail Image of Tutorial Fine-Tuning LLMs: When to Use SFT, RLHF, or DPO and How Quantization Affects Memory
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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NEW

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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NEW

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.
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NEW

Should You Fine-Tune Your LLM? Check Your Data First

Before you spend a dollar on compute, audit your data. That is the point. Fine-tuning changes the model itself. It shapes form, not facts: style, structure, behavior. It won't reliably inject knowledge that shifts week to week. Most teams asking how to fine-tune an LLM would get further by fixing…
Thumbnail Image of Tutorial Should You Fine-Tune Your LLM? Check Your Data First
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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How to Fine-Tune LLMs: LoRA Cuts VRAM 90%, Fits 7B on Consumer GPUs

Fine-tuning LLMs stopped being a cloud-only problem the moment LoRA arrived. Full fine-tuning pays the memory cost of training every weight in the network. LoRA freezes the base model and trains only small adapter matrices. QLoRA keeps that same adapter idea, then compresses the frozen base with…
Thumbnail Image of Tutorial How to Fine-Tune LLMs: LoRA Cuts VRAM 90%, Fits 7B on Consumer GPUs
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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nn.LayerNorm Explained: Pre-Norm Transformers, LoRA, and vLLM Inference

nn.LayerNorm is cheap enough that a single call never appears on a profile. The problem is volume. Every token, every layer, every forward pass repeats the same arithmetic. When you measure energy across training or inference, that repetition turns a tiny computation into a recurring cost. Research…
Thumbnail Image of Tutorial nn.LayerNorm Explained: Pre-Norm Transformers, LoRA, and vLLM Inference
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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Hugging Face TRL and PEFT: Choosing LoRA or QLoRA on One GPU

Watch: Fine-tuning LLMs with PEFT and LoRA by Sam Witteveen What actually separates LoRA from QLoRA? The split between LoRA and QLoRA is memory versus simplicity. Both freeze the base model and train tiny adapter matrices. QLoRA adds one thing on top: 4-bit quantization. That single change cuts…
Thumbnail Image of Tutorial Hugging Face TRL and PEFT: Choosing LoRA or QLoRA on One GPU
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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Multi Agent Deep RL with LoRA and QLoRA

Watch: LoRA & QLoRA Fine-tuning Explained In-Depth by Mark Hennings The demand for MARL has surged as industries seek solutions for dynamic, multi-participant environments. In robotics, agents coordinate tasks like warehouse logistics, where autonomous robots must manage shared spaces and avoid…
Thumbnail Image of Tutorial Multi Agent Deep RL with LoRA and QLoRA
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:
00
Read Full Article

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