lms — LM Studio's CLI

LM Studio ships with lms, a command line tool for scripting and automating your local LLM workflows.

lms is MIT Licensed and it is developed in this repository on GitHub: https://github.com/lmstudio-ai/lms

Install lms

lms ships with LM Studio and can be found under /bin in the LM Studio's working directory.

Use the following commands to add lms to your system path.

Bootstrap lms on macOS or Linux

Run the following command in your terminal:

~/.lmstudio/bin/lms bootstrap

Bootstrap lms on Windows

Run the following command in PowerShell:

cmd /c %USERPROFILE%/.lmstudio/bin/lms.exe bootstrap

Verify the installation

Open a new terminal window and run lms.

This is the current output you will get:

$ lms
lms - LM Studio CLI - v0.2.22
GitHub: https://github.com/lmstudio-ai/lmstudio-cli

Usage
lms <subcommand>

where <subcommand> can be one of:

- status - Prints the status of LM Studio
- server - Commands for managing the local server
- ls - List all downloaded models
- ps - List all loaded models
- load - Load a model
- unload - Unload a model
- create - Create a new project with scaffolding
- log - Log operations. Currently only supports streaming logs from LM Studio via `lms log stream`
- version - Prints the version of the CLI
- bootstrap - Bootstrap the CLI

For more help, try running `lms <subcommand> --help`

Use lms to automate and debug your workflows

Start and stop the local server

lms server start
lms server stop

List the local models on the machine

lms ls

This will reflect the current LM Studio models directory, which you set in 📂 My Models tab in the app.

List the currently loaded models

lms ps

Load a model (with options)

lms load [--gpu=max|auto|0.0-1.0] [--context-length=1-N]

--gpu=1.0 means 'attempt to offload 100% of the computation to the GPU'.

• Optionally, assign an identifier to your local LLM:

lms load TheBloke/phi-2-GGUF --identifier="gpt-4-turbo"

This is useful if you want to keep the model identifier consistent.

Unload models

lms unload [--all]