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LLMS
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5 Popular LLM Fine-Tuning Methods Visual Guide
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And those were the 5 popular LLM fine-tuning. Here's the visual again for your reference
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LoRA+: Optimizing Learning Rates for Matrix B in Fine-tuning
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5) LoRA+ – In LoRA, both matrices A and B are updated with the same learning rate.
– Authors of LoRA+ found that setting a higher learning rate for matrix B results in better convergence. Check this -
VeRA: Frozen Shared Matrices with Layer-Specific Scaling Vectors
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3) VeRA – In LoRA, low-rank matrices A and B are unique for each layer.
– In VeRA, A and B are frozen, random, and shared across all layers.
– Instead, it learns layer-specific scaling VECTORS (b and d) instead. Check this -
LoRA-FA: Optimizing Memory Usage by Freezing Matrix A
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2) LoRA-FA While LoRA significantly decreases the total trainable parameters, it requires substantial activation memory to update the low-rank weights. LoRA-FA (FA stands for Frozen-A) freezes matrix A and only updates matrix B. Check this
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LoRA: Efficient Fine-tuning with Low-Rank Matrices
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1) LoRA – Add two low-rank trainable matrices, A and B, alongside weight matrices.
– Instead of fine-tuning W, adjust the updates in these low-rank matrices. Even for the largest of LLMs, LoRA matrices take up a few MBs of memory. Check this -
Understanding Matrix Rank for Finetuning Techniques
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Now that we have a basic understanding of rank of a matrix, we're in a good position to understand the different finetuning techniques
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LLM Weights and Lower-Rank Adaptation in PEFT Techniques
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Some background! LLM weights are matrices of numbers adjusted during finetuning. Most PEFT techniques involve finding a lower-rank adaptation of these matrices—a smaller-dimensional matrix that can still represent the information stored in the original.
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Top 5 Parameter-Efficient Fine-Tuning Techniques for LLMs
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Traditional fine‑tuning is impractical for LLMs (billions of params; 100s GB). Since this kind of computing isn't accessible to everyone, parameter-efficient finetuning (PEFT) came into existence. Today, we’ll cover the top 5 PEFT techniques, step by step.
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Top 5 LLM Fine-Tuning Techniques Explained
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I have been fine-tuning LLMs for more that 2 years now! Here are the top 5 LLM fine-tuning techniques, explained with visuals: