Skip to main content

Lamini: Fine-Tune Your Large Language Models with Just 3 Lines of Code

Lamini: Fine-Tune Your Large Language Models with 3 Lines of Code

Everyone has been talking about Prompt Engineering! But it has several limitations! For example, the model always decides based on the training data (often a large amount of data from the Internet). Most models use general data. They are not specific to particular use cases. If you want to create large language models (LLMs) for specific use cases, you should think about fine-tuning.

In this article, we look at the LLM platform Lamini. Lamini is a platform for developers to build private and fine-tuned models. According to Lamini, you can fine-tune your LLM in three lines of code. Wow, that sounds incredible!

In our article, we’ll show you practical examples of when you should fine-tune your LLM. We also highlight the pros and cons of prompt engineering and fine-tuning. Be curious! You will learn many new things.

We’ll discuss the following points:

  • Setup of the environment

  • What is Fine-Tuning?

  • Prompt Engineering vs. Fine-Tuning

  • Fine-tuned LLM vs. Non-fine-tuned LLM

  • Procedure for Fine-Tuning

  • Fine-Tuning in practice with Lamini

  • Conclusion

To read this post you'll need to become a member. Members help us fund our work to ensure we can stick around long-term.

See our plans (Opens in a new window)

Topic Data Science


Would you like to be the first to write a comment?
Become a member of Tinz Twins Hub and start the conversation.
Become a member