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Comparing full fine-tuning and LoRA fine tuning
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Video Overview
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Comparing VRAM, Training Time + Quality
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How full fine-tuning works
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How LoRA works
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How QLoRA works
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How to choose learning rate, rank and alpha
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Choosing hyper parameters for Mistral 7B fine-tuning
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Specific tips for QLoRA, regularization and adapter merging.
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Tips for using Unsloth
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LoftQ - LoRA aware quantisation
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Step by step TinyLlama QLoRA
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Mistral 7B Fine-tuning Results Comparison
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Wrap up
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the differences between full fine-tuning and (Q)LoRA techniques in this comprehensive 53-minute video from Trelis Research. Learn about VRAM requirements, training time, and quality comparisons for various fine-tuning methods. Dive into the mechanics of full fine-tuning, LoRA, and QLoRA, and discover how to select optimal learning rates, ranks, and alpha values. Gain insights on hyperparameter selection for Mistral 7B fine-tuning, along with specific tips for QLoRA, regularization, and adapter merging. Explore the benefits of Unsloth and LoftQ for LoRA-aware quantization. Follow a step-by-step guide for TinyLlama QLoRA implementation and compare Mistral 7B fine-tuning results across different methods.

Full Fine-Tuning vs LoRA and QLoRA - Comparison and Best Practices

Trelis Research
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#Computer Science #Machine Learning #Fine-Tuning #Artificial Intelligence #Neural Networks #Deep Learning #Stable Diffusion #LoRA (Low-Rank Adaptation) #Hyperparameter Tuning #QLoRA #Mistral 7B
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Comparing full fine-tuning and LoRA fine tuning