DeepSeek Fine-Tuning Made Simple: Create Custom AI Models with Python

Author(s): Krishan Walia

Originally published on Towards AI.

Learn to fine-tune the DeepSeek R1 model for all your use cases.

This member-only story is on us. Upgrade to access all of Medium.

Not a member?Access the full article here (and don’t forget to leave a clap 👏🏻)

Why be late to leverage the best in class reasoning with this DeepSeek R1 model?

Fine Tune and use it for your awesome project🚀!!

While everyone’s racing to build applications on ChatGPT, savvy developers are quietly discovering DeepSeek-R1’s fine-tuning capabilities which is a hidden gem that turns a general-purpose AI into your specialized digital expert.

Through this article, you will learn how you can turn a general-purpose DeepSeek R1 model into a specialized, and domain-specific LLM.

There has been an emerging group of developers and founders that are not just discovering the latest and well-performing DeepSeek-R1 but are also looking out for ways by which they can integrate this model into their own products.

By fine-tuning the model we can make it possible to answer in a specialized and more domain-specific way. With the advanced reasoning capabilities of DeepSeek, it becomes an excellent choice for almost every task that involves thinking or problem-solving, all in a more organized and thoughtful manner.

In this article, we will be diving into the process of fine-tuning the DeepSeek-R1 model using Python. Through this article,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI