5 LLM Fine-tuning Techniques Explained Visually

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Fine-tuning LLMs

Traditional fine-tuning (depicted below) is infeasible with LLMs because these models have billions of parameters and are hundreds of GBs in size, and not everyone has access to such computing infrastructure.

Thankfully, today, we have many optimal ways to fine-tune LLMs, and five such popular techniques are depicted below:

We covered them in detail here:

• Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning.

• Implementing LoRA From Scratch for Fine-tuning LLMs.

Here’s a brief explanation:

• LoRA: Add two low-rank matrices A and B alongside weight matrices, which contain the trainable parameters. Instead of fine-tuning W, adjust the updates in these low-rank matrices.

• LoRA-FA: While LoRA considerably decreases the total trainable parameters, it still requires substantial activation memory to update the low-rank weights. LoRA-FA (FA stands for Frozen-A) freezes the matrix A and only updates matrix B.

• VeRA: In LoRA, every layer has a different pair of low-rank matrices A and B, and both matrices are trained. In VeRA, however, matrices A and B are frozen, random, and shared across all model layers. VeRA focuses on learning small, layer-specific scaling vectors, denoted as b and d, which are the only trainable parameters in this setup.

• Delta-LoRA: Here, in addition to training low-rank matrices, the matrix W is also adjusted but not in the traditional way. Instead, the difference (or delta) between the product of the low-rank matrices A and B in two consecutive training steps is added to W:

• LoRA+: In LoRA, both matrices A and B are updated with the same learning rate. Authors found that setting a higher learning rate for matrix B results in more optimal convergence.

To get into more detail about the precise steps, intuition, and results, read these articles:

• Understanding LoRA-derived Techniques for Optimal LLM Fine-tuning.

• Implementing LoRA From Scratch for Fine-tuning LLMs.

That said, these are not the only LLM fine-tuning techniques. The following visual depicts a timeline of popular approaches:

👉 Over to you: What are some ways to reduce the computational complexity of fine-tuning LLMs?

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