This is misinformation. Ollama’s supported structured outputs that conform to a given JSON-schema for months. Here’s a post about this from last year: https://ollama.com/blog/structured-outputs
This is absolutely possible to do at the model level via logit shaping. Llama-cpp’s functionality for this is called GBNF. It’s tightly integrated into the token sampling infrastructure, and is what ollama builds upon for their json schema functionality.
> It’s tightly integrated into the token sampling infrastructure, and is what ollama builds upon for their json schema functionality.
Do you mean the functionality of generating ebnf grammar and from a json schema use it for sampling is part of ggml, and all they have to do is use it?
I assumed that this was part of llama.cpp, and another feature they have to re-implement and maintain.
The whole point of GBNF is to serve as part of the API that lets downstream applications control token sampling in a high-level way without having to drop to raw logit distributions or pull model-specific tricks.
This thread was about doing structured generation in a model-agnostic way without wrapping try/except around json.parse(), and GBNF is _the_ way to do that.
This is absolutely possible to do at the model level via logit shaping. Llama-cpp’s functionality for this is called GBNF. It’s tightly integrated into the token sampling infrastructure, and is what ollama builds upon for their json schema functionality.