LMQL

What is LMQL?

LMQL (Language Model Query Language) is a programming language for interacting with language models. It provides a straightforward way to query these models, letting developers craft queries to extract specific information or generate exact outputs. The language supports querying model parameters, generating text, and completing prompts through a syntax accessible to programmers at any level of experience in natural language processing.

LMQL is compatible with various language models, including GPT-3 and GPT-4, allowing developers to draw on different model strengths depending on project needs. It also incorporates optimization techniques to speed up query performance, reduce latency, and keep interactions with language models running smoothly.

Beyond the language itself, LMQL offers an ecosystem of tools, libraries, documentation, tutorials, and an active community.

Who created LMQL?

LMQL was developed by the SRI Lab at ETH Zurich, along with contributions from others. It is built for efficiently querying and manipulating language models across a variety of applications.

What is LMQL used for?

• Content generation: Creating written material.
• Data analysis: Examining and interpreting data.
• Querying model parameters: Requesting specific settings or configurations of a model.
• Generating text and completing prompts: Producing written content and finishing user-provided inputs.
• Working with multiple models: Including GPT-3 and GPT-4.
• Optimizing performance: Making model requests faster and reducing latency.
• Portable LLM code: Automatically adapting code to work across different backends.
• Building AI applications: Chatbots, content generators, and data analysis tools.

Who is LMQL for?

• Developers: Anyone who writes code and wants structured interaction with language models.
• NLP professionals: Experts in how computers understand and process human language.

Getting started with LMQL

Install LMQL via the official documentation at lmql.ai, which includes tutorials, examples, and reference material. Once installed, you write queries in LMQL’s syntax to interact with supported models like GPT-3 and GPT-4 — specifying prompts, constraining outputs, and querying model parameters directly. The accompanying tools and libraries handle execution, and the community provides support for common questions.