1 min readfrom Machine Learning

[R] Fine-tuning services report

Our take

In the rapidly evolving landscape of AI and machine learning, fine-tuning services offer a practical solution for those with limited hardware who wish to train or deploy custom models. This report provides a comprehensive analysis of various providers, focusing on cost, speed, and user experience. With new entrants continuously reshaping the market, the ideal choice ultimately depends on your specific use case. Notably, Nebius stands out for its efficient capabilities in function-calling, enhancing the iteration process.

If you have some data and want to train or run a small custom model but don't have powerful enough hardware for training, fine-tuning services can be a good solution. Once training (requiring more resources than inference) is done, the custom model can then run locally. For larger models, there is also (for some providers) the option to run inference with the custom model using their services.

To get a better overview of the currently existing landscape, I did some benchmarking and experiments on cost, speed and user experience. The space is moving quickly, with new providers arriving even while I was testing, so what’s “best” really depends on your use case. For function-calling specifically, Nebius had some useful capabilities that made iteration more efficient.

Full write-up with details, methodology, and comparisons here: https://vintagedata.org/blog/posts/fine-tuning-as-service

submitted by /u/ynckdrt
[link] [comments]

Read on the original site

Open the publisher's page for the full experience

View original article

Related Articles