# How do you evaluate the performance of custom fine-tuned language models?

**URL:** <https://discuss.huggingface.co/t/how-do-you-evaluate-the-performance-of-custom-fine-tuned-language-models/176706>\
**Category:** Beginners\
**Created:** [June 11, 2026, 12:06pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-the-performance-of-custom-fine-tuned-language-models/176706 "2026-06-11T12:06:06Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![Suhebmultani](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/suhebmultani/32/49771_2.png) [@Suhebmultani](https://discuss.huggingface.co/u/Suhebmultani)\
**Post date:** [June 11, 2026, 12:06pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-the-performance-of-custom-fine-tuned-language-models/176706/1 "2026-06-11T12:06:06Z")

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After customizing (fine-tuning) a language model, how do you measure if it is working correctly and producing good results?

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**Author:** ![Bidram](https://avatars.discourse-cdn.com/v4/letter/b/71e660/32.png) [@Bidram](https://discuss.huggingface.co/u/Bidram)\
**Post date:** [June 11, 2026, 1:20pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-the-performance-of-custom-fine-tuned-language-models/176706/2 "2026-06-11T13:20:13Z")

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Hello!

As far as I understand, the first step is to clearly identify the weaknesses of the original model and define exactly what you want the fine-tuning to improve. Without that, it becomes very hard to tell whether the customization actually helped or just changed the model’s behavior.

After that, it is important to build a fixed and consistent benchmark that covers both the areas where the base model struggles and the areas where it already performs well. This gives you a more balanced evaluation and helps you see not only where the fine-tuned model improved, but also whether it regressed in other places.

Then you can evaluate both the original model and the fine-tuned model on the same benchmark under the same conditions and compare the results carefully. That comparison will show much more clearly whether the fine-tuning produced a real improvement, had little practical effect, or introduced new weaknesses.

In short, the general process is:

- identify the weakness in the base model
- define the exact improvement you want
- create a consistent evaluation benchmark
- test both models under the same conditions
- compare the results carefully and objectively

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**Author:** ![lea97338](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/lea97338/32/60736_2.png) [@lea97338](https://discuss.huggingface.co/u/lea97338)\
**Post date:** [June 11, 2026, 6:12pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-the-performance-of-custom-fine-tuned-language-models/176706/3 "2026-06-11T18:12:52Z")

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Just use a hard prompt and very strict prompt