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6 changes: 4 additions & 2 deletions chapters/en/chapter1/4.mdx
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Expand Up @@ -121,8 +121,10 @@ This pretraining is usually done on very large amounts of data. Therefore, it re
For example, one could leverage a pretrained model trained on the English language and then fine-tune it on an arXiv corpus, resulting in a science/research-based model. The fine-tuning will only require a limited amount of data: the knowledge the pretrained model has acquired is "transferred," hence the term *transfer learning*.

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<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/finetuning.svg" alt="The fine-tuning of a language model is cheaper than pretraining in both time and money.">
<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/finetuning-dark.svg" alt="The fine-tuning of a language model is cheaper than pretraining in both time and money.">

<img class="block dark:hidden" src="../images/finetuning_updated.svg" alt="The fine-tuning of a language model is cheaper than pretraining in both time and money.">

<img class="hidden dark:block" src="../images/finetuning_darkupdated.svg" alt="The fine-tuning of a language model is cheaper than pretraining in both time and money.">
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Fine-tuning a model therefore has lower time, data, financial, and environmental costs. It is also quicker and easier to iterate over different fine-tuning schemes, as the training is less constraining than a full pretraining.
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