The lms load command loads a model into memory. You can optionally set parameters such as context length, GPU offload, and TTL.

Parameters

[path] (optional) : string

The path of the model to load. If not provided, you will be prompted to select one

--ttl (optional) : number

If provided, when the model is not used for this number of seconds, it will be unloaded

--gpu (optional) : string

How much to offload to the GPU. Values: 0-1, off, max

--context-length (optional) : number

The number of tokens to consider as context when generating text

--identifier (optional) : string

The identifier to assign to the loaded model for API reference

Load a model

Load a model into memory by running the following command:

lms load <model_key>

You can find the model_key by first running lms ls to list your locally downloaded models.

Set a custom identifier

Optionally, you can assign a custom identifier to the loaded model for API reference:

lms load <model_key> --identifier "my-custom-identifier"

You will then be able to refer to this model by the identifier my_model in subsequent commands and API calls (model parameter).

Set context length

You can set the context length when loading a model using the --context-length flag:

lms load <model_key> --context-length 4096

This determines how many tokens the model will consider as context when generating text.

Set GPU offload

Control GPU memory usage with the --gpu flag:

lms load <model_key> --gpu 0.5    # Offload 50% of layers to GPU
lms load <model_key> --gpu max    # Offload all layers to GPU
lms load <model_key> --gpu off    # Disable GPU offloading

If not specified, LM Studio will automatically determine optimal GPU usage.

Set TTL

Set an auto-unload timer with the --ttl flag (in seconds):

lms load <model_key> --ttl 3600   # Unload after 1 hour of inactivity

Operate on a remote LM Studio instance

lms load supports the --host flag to connect to a remote LM Studio instance.

lms load <model_key> --host <host>

For this to work, the remote LM Studio instance must be running and accessible from your local machine, e.g. be accessible on the same subnet.