Discussions about Predibase

Fast experiment loops matter. How quickly can you go from a new dataset to a deployed adapter on [Predibase](https://saaskart.co/software/predibase)?

At high volume, per-token API pricing hurts. How did serving a fine-tune on [Predibase](https://saaskart.co/software/predibase) compare on total cost for your w

Small fine-tuned models are great at extraction. How is [Predibase](https://saaskart.co/software/predibase) performing on JSON or entity extraction versus a lar

Fine-tuning means sending training data somewhere. What controls does [Predibase](https://saaskart.co/software/predibase) offer, and are teams using VPC deploym

After fine-tuning on [Predibase](https://saaskart.co/software/predibase), how are you measuring whether the new model is actually better on your real distributi

Llama, Mistral, and others are all options on [Predibase](https://saaskart.co/software/predibase). How are you picking a base model for a given task and budget?

You can fine-tune with raw Hugging Face and PEFT. What did [Predibase](https://saaskart.co/software/predibase) save you versus rolling your own training and ser

The multi-adapter serving in [Predibase](https://saaskart.co/software/predibase) promises big savings. How many adapters are people packing onto one base model

A common blocker is dataset size. For tasks fine-tuned on [Predibase](https://saaskart.co/software/predibase), how many examples did it take to beat a prompted

Prompt engineering a large API model is easy, but [Predibase](https://saaskart.co/software/predibase) argues fine-tuning a small model wins on cost and latency.