# Fine Tuning

- Page: https://shared.btbytes.com/p/fine-tuning
- Date: 2024-04-05
- Tags: llm

[Why fine tune?](https://cohere.com/fine-tuning)

- *Greater Accuracy* -- By tailoring the model to specific use cases and industries, it can better understand and generate contextually relevant responses.
- *Improve Efficiency* -- Fine-tuning streamlines performance by reducing token usage and condensing the effectiveness of a larger model into a smaller, more efficient one.

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Fine-tuning tailors the LLM's weights to specific domains and tasks, improving performance on long-term challenges. [via](https://www.linkedin.com/posts/armand-ruiz_a-customer-asked-me-why-fine-tuning-at-all-activity-7362800429593739264-K_Xo)


## Tutorials

[Fine-Tuning LLMs: A Guide With Examples ](https://www.datacamp.com/tutorial/fine-tuning-large-language-models);  and [How to Fine Tune GPT 3.5](https://www.datacamp.com/tutorial/how-to-fine-tune-gpt3-5)



## Notes

some good experience shared here: https://x.com/MaximeRivest/status/1895315348544483633 in response to https://x.com/simonw/status/1895301139819860202

`gemma-3-270m` might be a good model to finetune; should be possible to do it on my MBP.