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Fine-Tuning vs Distillation vs Transfer Learning: What’s The Difference?
What are the main ideas behind fine-tuning, distillation, and transfer learning? A simple explanation with a focus on LLMs.
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With the launch of Deepseek-R1 and its distilled models, many ML engineers are wondering: what’s the difference between distillation and fine-tuning? And why has transfer learning, very popular before the rise of LLMs, seemingly became forgotten?
In this article, we’ll look into their differences and determine which approach is best suited for which situations.
Note: While this article is focused on LLMs, these concepts apply to other AI models as well.
1. Fine-tuning
Although this method was used long before the era of LLMs, it gained immense popularity after the arrival of ChatGPT. It’s easy to see the reason behind this rise if you know what GPT stands for — ‘Generative Pre-trained Transformer.’ The ‘pre-trained’ part indicates that the model was trained already, but it can be further trained for specific goals. That’s where fine-tuning comes in.