Beyond LoRA: Can you beat the most popular fine-tuning technique?

What changed

If you want to fine-tune an open model on your own data, you are probably interested in so-called parameter-efficient fine-tuning, in short PEFT. This term describes techniques that significantly reduce the memory requirement to fine-tune a model. Although there are dozens of these techniques, almost everyone chooses one called “LoRA”.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Using LoRA for Efficient Stable Diffusion Fine-Tuning
• (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware
• Fine-tuning now available for GPT-4o

• Using LoRA for Efficient Stable Diffusion Fine-Tuning similarity 0.32
• (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware similarity 0.28
• Fine-tuning now available for GPT-4o similarity 0.21