lmql VS simpleAI

have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.

the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.

lqml

This is also similar in spirit to LMQL

https://github.com/eth-sri/lmql

LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.

Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.

LMQL is another.

Not related to my own project SimpleAI despite the name, but looks like we can easily make the two work together, to keep it « simple ». Nice work!

Using this as an opportunity to mention my own related project, perhaps it can end up on your nice list one day. :)

https://github.com/lhenault/SimpleAI

You could have a look at a project I’ve been working on, SimpleAI, doing exactly this by replicating the OpenAI endpoints (you can then use their JS client for integration). Adding StableLM should be straightforward, I plan to add it to the examples in the upcoming days once I have a bit of time.

I’ve built SimpleAI with exactly these kinds of use cases in mind. That should allow supporting any model with minimal / no change to your project. Good job and good luck with LoopGPT, that looks nice!

You could give this a shot: https://github.com/lhenault/simpleAI

I don't know if this applies to your use case but this would probably work if you are looking for an llm to help with programming. Haven't really played around with it but this may work for general llm tasks, it doesn't have a web UI though.

As per llama.cpp specifically, you can indeed add any model, it's just a matter of doing a bit of glue code and declaring it in your models.toml config. It's quite straightforward thanks to some provided tools for Python (see here for instance). For any other language it's a matter of integrating it through the gRPC interface (which shouldn't be too hard for Llama.cpp if you're comfortable in C++). I'm planning to also add support for REST for model in the backend at some point too.

Shameless plug but I’ve been recently working on SimpleAI, a project replicating the main endpoints from OpenAI API, allowing you to seamlessly switch from their API to your own one, as it’s compatible with OpenAI client.

I wanted to share with you SimpleAI, a self-hosted alternative to OpenAI API.