Controllable Generation
HeimdaLLM
Pronounced [ˈhaɪm.dɔl.əm] or HEIM-dall-EM
HeimdaLLM is a robust static analysis framework for validating that LLM-generated structured output is safe. It currently supports SQL.
In simple terms, it helps makes sure that AI won't wreck your systems.
Pronounced [ˈhaɪm.dɔl.əm] or HEIM-dall-EM
HeimdaLLM is a robust static analysis framework for validating that LLM-generated structured output is safe. It currently supports SQL.
In simple terms, it helps makes sure that AI won't wreck your systems.
amoffat • GitHub - amoffat/HeimdaLLM: Verify LLM output
LMQL is a programming language for LLMs.Robust and modular LLM prompting using types, templates, constraints and an optimizing runtime.
LMQL is a programming language for LLM interaction. | LMQL
SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system.
The core features of SGLang include:
The core features of SGLang include:
- A Flexible Front-End Language : This allows for easy programming of LLM applications with multiple
sgl-project • GitHub - sgl-project/sglang: SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.
They have a fast jsond ecoding feature with a finite state machine.
Some techniques I have seen in the wild
this style was popular back in the early days of gpt-3.5 when it landed, for some reason, it fell out of fashion
Best example of this is the... See more
- Create a semi-strict programming language like promptlang - https://github.com/ruvnet/promptlang
this style was popular back in the early days of gpt-3.5 when it landed, for some reason, it fell out of fashion
- Write a super long persona mapping prompt with a well defined action space
Best example of this is the... See more
r/LocalLLaMA - Reddit
Super JSON Mode is a Python framework that enables the efficient creation of structured output from an LLM by breaking up a target schema into atomic components and then performing generations in parallel.
It supports both state of the art LLMs via OpenAI 's legacy completions API and open source LLMs such as via Hugging Face Transformers and vLLM .... See more
It supports both state of the art LLMs via OpenAI 's legacy completions API and open source LLMs such as via Hugging Face Transformers and vLLM .... See more