Unload a model from memory via REST API

Usage

lms_unload(model, host = "http://localhost:1234", ..., token = NULL)

Arguments

model: Character. Unique identifier (instance_id) of the model instance to unload. Must be one name, given as a single string. The string must be valid in its declared encoding and not marked "bytes". A class, names, and the S4 bit are removed before the name is sent.
host: Character. The host address of the local server. Defaults to "http://localhost:1234".
...: Additional arguments passed to the API request body.
token: Character or NULL. An API token for a server that requires authentication. NULL reads the rlmstudio.token option and then the RLMSTUDIO_API_TOKEN environment variable. See rlmstudio_token.

Value

Invisibly returns a character string representing the unloaded instance_id upon success. It is a plain string, with no class, names, or S4 bit.

Note

If you have loaded multiple instances of the same model using force = TRUE in lms_load(), the server assigns them unique instance identifiers (e.g., "google/gemma-3-1b" and "google/gemma-3-1b:2"). Passing the base model name to lms_unload() will only unload the primary instance. To unload duplicate instances, you must provide their exact instance_id, or use lms_unload_all() to clear everything.

Server not running

Functions that call the LM Studio REST API open a TCP connection to the hostname and port named in host before they send the request. A function that checks its own arguments does that first, so a bad model, job_id, input, inputs, messages, schema, ttl, previous_response_id, or batch_size, a bad type of list_models() or list_instances(), a bad TRUE or FALSE argument such as simplify, logprobs, quiet, force, or store, a store on the "openai" route of lms_chat() or lms_chat_batch(), a stream in the ... of a chat function, a logprobs in the ... of lms_chat_batch() or lms_chat_native(), a value in the ... of lms_chat_batch() that lms_chat() reads as an argument already given, an instructions or messages in the ... of lms_chat() or lms_chat_batch() on the route where lms_chat() sets it, or a name, id, or type string that is not valid in its encoding or is marked as bytes, aborts with an argument message and no condition class even when the server is down. A condition of class rlmstudio_no_server is raised when that connection cannot be opened. A refused connection raises it. So do an address the package cannot parse and a hostname that does not resolve. An address that neither accepts nor refuses the connection also raises it. That case waits for the operating system to give up, which can take a minute. Start the server with lms_server_start(), or give host the address that your server listens on.

The check reads the port and nothing else. Any process holding that port accepts the connection, so the condition is not raised even though no LM Studio server is there. The call then does not raise rlmstudio_no_server, and what it does depends on what answers. On a status-200 body that does not parse as JSON, the chat functions, lms_embed(), list_models(), list_instances(), lms_load(), lms_download(), lms_download_status(), and lms_unload_all() raise rlmstudio_bad_response. lms_unload() does not read the body, so it can report success. A body that parses as JSON but has another shape can come back unchanged with simplify = FALSE. With simplify = TRUE, the chat functions and lms_embed() raise rlmstudio_bad_response for it. list_models() and list_instances() raise it for a model list with another shape, and so do lms_unload_all() and lms_load() without force = TRUE, which read that list. lms_chat_openai() and lms_chat_openresponses() raise it for such a model list too, with either setting of simplify, through the model lookup that a reply from another model starts. lms_chat() raises it through them. lms_load(), lms_download(), and lms_download_status() raise it for a reply of their own with another shape, such as {}. A process that does not answer in HTTP gives an httr2_failure error. Use lms_server_ready() for the stronger test: it asks the host for a model list and reports TRUE only for a model list that list_models() can read.

lms_chat_batch() checks the server once before its first input, and lms_chat() checks it again for each input. If that check finds the server gone during the batch, the batch aborts with rlmstudio_no_server, and no request goes out after that. The condition then carries a results field, a list as long as inputs. Its elements before the lost input hold the values that format = "list" returns for those inputs. The element of the lost input and every element after it are NULL. The check before the first input adds no results field. A connection that fails after the check passes, such as a server that stops during a request, raises an httr2_failure error instead. That error aborts the batch and carries no results field.

lms_embed() checks the server before each request, and a request carries at most batch_size inputs. If a check after the first request finds the server gone, the call aborts with rlmstudio_no_server. Once a request has succeeded, the condition carries a results field. With simplify = TRUE, results is a matrix with NA in each row whose embedding did not arrive. With simplify = FALSE, it is a list with one element per batch, with NULL in the element of the request that ended the call and in every element after it.

API failure

A condition of class rlmstudio_api_error is raised when a REST call returns a response that the wrapper treats as a failure. The condition carries a status field, which holds the HTTP response status as an integer. It also carries a code field. The field holds the string at error.code of the response body, such as "model_not_found". It is NULL when the body does not parse, when error is not a JSON object, or when its code is not one string.

lms_chat_batch() aborts on it when its status is 401, 403, or 404, and when its status is 400 and its code is "model_not_found". No request goes out after that input. The condition then carries a results field that follows the rule for a lost server in the "Server not running" section: its elements before the failed input hold the values that format = "list" returns for those inputs, and the element of the failed input and every element after it are NULL. For any other status, the element of the failed input holds the condition, or NA where the result is text, and the batch warns once and goes on. See the details of lms_chat_batch().

lms_embed() follows the same rule for each request. A 401, 403, or 404 aborts the call, and once a request has succeeded the condition carries a results field, a matrix or a list as the "Server not running" section describes. Any other status fails the inputs of that request alone, and the call warns once and goes on. If every request fails, the call aborts with the first condition. See the details of lms_embed().

Examples

if (FALSE) { # \dontrun{
lms_server_start()
lms_download("google/gemma-3-1b")
lms_load("google/gemma-3-1b")

# Unload a single specific model
lms_unload("google/gemma-3-1b")
} # }