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

Beyond LoRA: Can you beat the most popular fine-tuning technique? Beyond LoRA: Can you beat the most popular fine-tuning technique? Published June 18, 2026 Update on GitHub Upvote 103 Benjamin Bossan BenjaminB Sayak Paul sayakpaul Marian Tietz hubnemo Kashif Rasul kashif When you plan to fine-tune a model in a parameter-efficient way, think beyond LoRA 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 .

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• Beyond LoRA: Can you beat the most popular fine-tuning technique?
• Published June 18, 2026 Update on GitHub Upvote 103 Benjamin Bossan BenjaminB Sayak Paul sayakpaul Marian Tietz hubnemo Kashif Rasul kashif When you plan to fine-tune a model in a parameter-efficient way, think beyond LoRA 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”.
• In this blog post, we explore whether LoRA is really the best choice, what tools are available to make an informed decision, and how you can benefit from extending your horizon beyond LoRA.
• What is PEFT and when do you need it There are countless open models available, but they often aren't quite good enough for your use case.