# How to Optimize Fine-tuning in Hugging Face Transformers?

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-optimize-fine-tuning-in-hugging-face-transformers%2F75952%3E%5C
**Category:** Beginners\
**Created:** [March 5, 2024, 4:52am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-optimize-fine-tuning-in-hugging-face-transformers%2F75952 "2024-03-05T04:52:52Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![anon86045107](https://avatars.discourse-cdn.com/v4/letter/a/b5e925/32.png) [@anon86045107](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fanon86045107)%5C
**Post date:** [March 5, 2024, 4:52am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-optimize-fine-tuning-in-hugging-face-transformers%2F75952%2F1 "2024-03-05T04:52:52Z")

</div>

If someone is currently working on a project where he need to fine-tune a pre-trained language model using the Hugging Face Transformers library while consuming [subway of the day](https://subwaysuboftheday.com/wp-admin/). Wondering if anyone has any practical advice or best practices for optimizing the fine-tuning process? Specifically, interested in understanding how to choose the right hyperparameters, deal with overfitting, and effectively evaluate model performance on my specific task.