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Installation Instructions

To install LMQL locally, run the following command in a Python >=3.10 environment.

pip install lmql

Local GPU Support: If you want to run models on a local GPU, make sure to install LMQL in an environment with a GPU-enabled installation of PyTorch >= 1.11.

To install LMQL with GPU dependencies via pip, run pip install lmql[hf].


For quick experimentation, you can also use the web-based Playground IDE

Running LMQL Programs


After installation, you can launch a local instance of the Playground IDE using the following command:

lmql playground


Using the LMQL playground requires an installation of Node.js. If you are in a conda-managed environment you can install node.js via conda install nodejs=14.20 -c conda-forge. Otherwise, please see the official Node.js website for instructions on how to install it on your system.

This launches a browser-based Playground IDE, including a showcase of many exemplary LMQL programs. If the IDE does not launch automatically, go to http://localhost:3000.

Command-Line Interface

As an alternative to the playground, the command-line tool lmql run can be used to execute local .lmql files.

Python Integration

LMQL can also be used directly from within Python. To use LMQL in Python, you can import the lmql package, run query code via or use a decorator @lmql.query for LMQL query functions.

For more details, please see the Python Integration chapter.

Self-Hosted Models

Note that when using local 🤗 Transformers models in the Playground IDE or via lmql run, you have to first launch an instance of the LMQL Inference API for the corresponding model via the command lmql serve-model.

For more details, please see 🤗 Models chapter.

Configuring OpenAI API Credentials

If you want to use OpenAI models, you have to configure your API credentials. To do so you can either define the OPENAI_API_KEY environment variable or create a file api.env in the active working directory, with the following contents.

openai-org: <org identifier>
openai-secret: <api secret>

For system-wide configuration, you can also create an api.env file at $HOME/.lmql/api.env or at the project root of your LMQL distribution (e.g. src/ in a development copy).