[FlyingLawnmower]

I spent a couple years building a high performance, expressive library for structured outputs in LLMs. Our library is used by OpenAI for structured outputs on the hosted API. Happy to answer questions on how this works:

User friendly library that connects to lots of OSS model serving backends: https://github.com/guidance-ai/guidance/

Core Rust library written for high performance mask computation (written mostly by my collaborator @mmoskal): http://github.com/guidance-ai/llguidance

[btown]

The LLGuidance paper is highly recommended reading for everyone interested in this! https://guidance-ai.github.io/llguidance/llg-go-brrr

TL;DR instead of just getting a token and seeing if it would be accepted by the parser, you can actually zero-out probabilities for all invalid tokens, and do the computation for this in parallel at effectively zero cost:

> Here, compute_mask() can run on the CPU during the time it would be normally just waiting for the GPU to finish. The line prob[~mask] = 0.0 would normally be fused into the softmax kernel in the last stage of the LLM, with negligible overhead. Therefore, as long as the compute_mask() function completes faster than the LLM forward pass and parser.consume() is negligible (typically follows from compute_mask() speed), the constrained generation will be as fast as the unconstrained one.

I'm curious - have there been any research/conversations about pushing masking even earlier in the pipeline? In theory, there's a fair amount of compute that goes into computing the probability of tokens that will end up being masked away anyways.

[lelanthran]

> Happy to answer questions on how this works

Well, thank you for that; from a quick skim of Guidance, it looks like it is used when interfacing with the model directly - i.e. if I want to use Guidance I can't simply send input to my local Ollama instance, I have to stand up a small Python program that loads the model, accepts input from the user, push the user input tokens into the model, and for each output token, reject it if it fails some criteria.

Is this correct? If so, it means that the current way LLMs are interfaced with (via stdin/stout or an HTTP endpoint) can't be used with something like Guidance, correct?

[stillsut]

I'm also working on a library to steer the sampling step of LLM's but more for steganographic / arbitrary data encoding purposes.

Should work with any llama.cpp compatible model: https://github.com/sutt/innocuous

[dcreater]

i am not following how you encoded a BTC address into a poem. can you help explain?

[ru552]

"The constraint system offered by Guidance is extremely powerful. It can ensure that the output conforms to any context free grammar (so long as the backend LLM has full support for Guidance). More on this below." --from https://github.com/guidance-ai/guidance/

I didn't find any more on that comment below. Is there a list of supported LLMs?

[FlyingLawnmower]

Good point re: documentation...

We have support for Huggingface Transformers, llama.cpp, vLLM, SGLang, and TensorRT-LLM, along with some smaller providers (e.g. mistral.rs). Using any of these libraries as an inference host means you can use an OSS model with the guidance backend for full support. Most open source models will run on at least one of these backends (with vLLM probably being the most popular hosted solution, and transformers/llama.cpp being the most popular local model solutions)

We're also the backend used by OpenAI/Azure OpenAI for structured outputs on the closed source model side.

[dcreater]

How does this compare to pydantic ai?

I'm yet to see a thorough comparison of design, performance and reliability between these options (along with outlines etc)

[FlyingLawnmower]

We did quite a thorough benchmarking of various structured decoding providers in one of our papers: https://arxiv.org/abs/2501.10868v3 , measuring structured outputs providers on performance, constraint flexibility, downstream task accuracy, etc.

Happy to chat more about the benchmark. Note that these are a bit out of date though, I'm sure many of the providers we tested have made improvements (and some have switched to wholesale using llguidance as a backend)

[_andrei_]

pydantic is a _validation_ library, it does not do any kind of constraints by itself

[Balgair]

I'm stupid, so my question will be too.

I'm trying to write a really large book. I have a lot of material that I'm using RAG to help manage. I put into my prompts the top RAG cosine scores with some summaries of characters and previous chapters and scene sketches. I get scenes out and then work them over. LLMs are really helpful for my disability and have allowed me to make any progress at all on this.

Is your thing something I should look into for helping keep track of my material. I'm using Excel sheets and crappy python code right now.

Im pretty sure your stuff is some super technical backend thingy, but I figured I'd shoot my shot here. Thanks for any and all info, I appreciate it

[ijk]

I've been curious about grammar support for non-JSON applications. (i.e., I have some use cases where XML is more natural and easier to parse but Pydantic seems to assume you should only work with JSON.) Would guidance be able to handle this use case?

In general I find that matching the most natural format for a document outperforms waiting for the big model trainers to convince the model that the format you want is a valid structure, so anything that lets me interweave structured and unstructured generation is very interesting to me right now.

[FlyingLawnmower]

guidance can handle many context-free grammars. We use an Earley parser under the hood (https://en.wikipedia.org/wiki/Earley_parser) which gives us significant flexibility boosts over alternative approaches that use weaker parsers (and went through lots of effort to make Earley parsing fast enough to not slow down LM inference). However, XML is not perfectly context-free, though with some basic assumptions you can make it CF.

The annoying bit with grammars is that they are unfortunately a bit complex to write properly. Fortunately language models are getting better at this, so hopefully to get an XML grammar, you can get most of the way there with just a GPT-5 prompt. Suppose it would be a good idea to have a better pre-built set of popular grammars (like a modified XML) in guidance so that we cut this headache out for users...!

[FlyingLawnmower]

Thanks :)

Great question re: adoption...it's definitely dominated by JSON. Most API providers have standardized on JSON outputs, so application teams have started building shims that map other formats to JSON and back. Similarly, with models heavily being post-trained to generate "good" JSON, I think there's a better model-constraint alignment story with JSON than most arbitrary grammars.

That said, internally, we experiment quite a lot with custom grammars all across the stack. It's more complicated to write a grammar than a JSON schema (though LMs are very good at grammar writing now) and more error prone to debug, but it can help significantly in certain cases (e.g. having models write custom DSLs not commonly found on the internet, at various parts of a model training pipeline, etc. etc.). I'm hoping that with the right tooling around it, the broader community will start nudging beyond JSON.

To that end, the python guidance library is really an attempt to make writing grammars more friendly to a python programmer. More to be done here of course!