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Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)
This article shares practical tips for fine tuning large language models with low rank adaptation, drawn from experiments.
About Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)
This is an article by Sebastian Raschka, PhD, published November 19, 2023, titled Practical Tips for Finetuning LLMs Using LoRA. It summarizes lessons from hundreds of experiments with low-rank adaptation, based in part on work with the open-source Lit-GPT repository.
Readers can review main takeaways on training consistency, memory use, optimizer choice, overfitting, and LoRA settings such as rank, alpha, and layer coverage. The article also provides a brief introduction to how LoRA decomposes weight updates, and answers ten common questions on datasets, domain adaptation, memory, and comparison to full finetuning and RLHF.
It suits readers interested in open-source LLMs and custom model finetuning who want experiment-based observations rather than a code implementation. The text focuses on broader ideas and takeaways from working with LoRA.
Key features
- Explains LoRA weight update with decomposed matrices
- Lists takeaways from multiple LoRA training experiments
- Answers ten common questions about LoRA use
- Covers optimizer choice and memory usage factors
- Discusses LoRA rank, alpha, and layer coverage
Best for: People interested in finetuning open-source LLMs with LoRA benefit most.
Good for
- Drawn from hundreds of LoRA experiments
- Addresses rank, alpha, optimizers, and memory use
- Includes ten specific reader questions with answers
Watch out for
- Focuses on concepts, not from-scratch code implementation
Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation) at a glance
- Official website
- magazine.sebastianraschka.com/p/practical-tips…
- Type
- Publication
- Platforms
- Web
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation) FAQ
What is LoRA in LLM finetuning?
It is a method that learns weight changes as two smaller matrices instead of computing the full update matrix, which reduces compute and memory needs.
What rank and alpha values does the article suggest for LoRA?
It says adjusting both is important, with a heuristic of setting alpha at twice the rank value.
Should LoRA be applied to all layers?
The article advises applying LoRA across all layers, not only to Key and Value matrices, to maximize model performance.
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