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.
