tgaddair

In LoRAX v0.8 we've added native integration with Outlines, allowing you to guarantee your output always comes back in the structure of your choosing.

But while structured generation can guarantee the right format comes back, it cannot always guarantee that the properties returned have the right content in them. This is where fine-tuning comes in.

With LoRAX, combining both approaches together during inference is as easy as specifying two parameters in LoRAX: a "schema" and a fine-tuned LoRA "adapter_id". Together, you get the best of both worlds: the right format and the right content.

If getting reliable JSON output from LLMs is something you're interested in, do check out the blog for more details, including a tutorial, public LoRA adapter hosted on HuggingFace, and the complete set of benchmarking scripts to reproduce our results.

Yes, we (LoRAX devs) saw that (we know the author pretty well). It's a useful addition, though quite a bit simpler than our level of support for multi-LoRA inference. We're planning on doing a more comprehensive comparison soon, now that it's officially out.

I will say that if you want to explore the forefront of this multi-LoRA inference, definitely worth giving LoRAX a look. We just added support for per-request model merging (https://predibase.github.io/lorax/guides/merging_adapters/) as an example, and are planning on continuing to double down on this idea of combining adapters in some pretty unique ways.

Thanks for the shoutout! We are adding the Outlines integration to LoRAX in the next two weeks, so stay tuned!

Hey, LoRAX dev here. This was one thing we spent a lot of effort optimizing. The TL;DR is that in most cases latency will be with 80% of the baseline latency with 0 adapters with as many as 128 adapters at once under heavy request load. Check out the section Results in the blog for more details and let me know if you have any questions!