Package {rlmstudio}

Title:	Access and Control LM Studio
Version:	0.2.2
Description:	A community-maintained 'R' wrapper for the 'LM Studio' command line interface and API. Provides functions to manage the local daemon and server, download and load models, and interact with Large Language Models (LLMs).
License:	MIT + file LICENSE
URL:	https://jmgirard.github.io/rlmstudio/, https://github.com/jmgirard/rlmstudio
BugReports:	https://github.com/jmgirard/rlmstudio/issues
Depends:	R (≥ 4.1.0)
Imports:	cli, httr2, jsonlite, processx, utils
Suggests:	httptest2, knitr, mockery, rmarkdown, testthat (≥ 3.0.0), withr
VignetteBuilder:	knitr
Config/testthat/edition:	3
Encoding:	UTF-8
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-05-02 18:24:44 UTC; jmgirard
Author:	Jeffrey Girard [aut, cph, cre]
Maintainer:	Jeffrey Girard <me@jmgirard.com>
Repository:	CRAN
Date/Publication:	2026-05-05 18:30:02 UTC

rlmstudio: Access and Control LM Studio

Description

A community-maintained 'R' wrapper for the 'LM Studio' command line interface and API. Provides functions to manage the local daemon and server, download and load models, and interact with Large Language Models (LLMs).

Author(s)

Maintainer: Jeffrey Girard me@jmgirard.com (ORCID) [copyright holder]

Authors:

• Jeffrey Girard me@jmgirard.com (ORCID) [copyright holder]

See Also

Useful links:

• https://jmgirard.github.io/rlmstudio/

• https://github.com/jmgirard/rlmstudio

• Report bugs at https://github.com/jmgirard/rlmstudio/issues

Check if the installed LM Studio CLI meets the minimum requirement

Description

Check if the installed LM Studio CLI meets the minimum requirement

Usage

check_lms_version(min_version = "0.4.0")

Arguments

Value

A logical scalar: TRUE if the LM Studio CLI version meets or exceeds the specified min_version, and FALSE otherwise.

Examples

## Not run: 
check_lms_version("0.4.0")

## End(Not run)

Check if LM Studio CLI is installed

Description

Check if LM Studio CLI is installed

Usage

has_lms()

Value

A logical scalar: TRUE if the lms executable is found on the system path, and FALSE otherwise.

Examples

## Not run: 
has_lms()

## End(Not run)

Help the user install or update LM Studio

Description

This function provides two methods for setting up LM Studio on your system. The "browser" method opens the official download page for the LM Studio desktop application (GUI). The "headless" method runs an automated installation script to install the llmster daemon and CLI, which is suitable for servers, containers, or users who prefer a GUI-less environment.

Usage

install_lmstudio(method = c("browser", "headless"))

Arguments

Value

Invisibly returns TRUE upon successful completion. This function is primarily utilized for its side effects of opening a web browser or executing system installation commands.

Examples

## Not run: 
# Open your default web browser to the download page
install_lmstudio(method = "browser")

# Attempt automatic headless installation via the command line
install_lmstudio(method = "headless")

## End(Not run)

List available models

Description

Retrieves a list of models available on your system via the LM Studio REST API.

Usage

list_models(
  loaded = FALSE,
  type = c("llm", "embedding"),
  detailed = FALSE,
  quiet = FALSE,
  host = "http://localhost:1234"
)

Arguments

Value

A data.frame containing information about the available models. By default, it includes columns for state, type, display_name, key, architecture, and size_gb. If detailed = TRUE, it returns a comprehensive data.frame including all raw metadata columns provided by the API. Returns an empty data.frame if no models match the criteria.

See Also

LM Studio List Models API

Examples

## Not run: 
lms_server_start()
lms_download("google/gemma-3-1b")
lms_load("google/gemma-3-1b")

# List all downloaded models
list_models()

# List only currently loaded models
list_models(loaded = TRUE)

# Get detailed information about loaded text models
list_models(loaded = TRUE, type = "llm", detailed = TRUE)

## End(Not run)

Chat Completion with LM Studio

Description

Send a prompt to a locally running LM Studio model. This wrapper automatically routes your request to the appropriate subfunction based on the selected API type.

Usage

lms_chat(
  model,
  input,
  system_prompt = NULL,
  host = "http://localhost:1234",
  api_type = c("openresponses", "openai", "native"),
  logprobs = FALSE,
  simplify = TRUE,
  ...
)

Arguments

Value

Depending on the arguments provided:

• If simplify = FALSE, returns a parsed list of the raw JSON response.

• If simplify = TRUE and logprobs = FALSE, returns a single character string containing the model's text response.

• If simplify = TRUE and logprobs = TRUE (and the chosen API type supports it), returns an object of class lms_chat_result containing both the text and a data.frame of token probabilities.

Batch Chat Completion with LM Studio

Description

Process a vector of inputs sequentially through LM Studio.

Usage

lms_chat_batch(
  model,
  inputs,
  system_prompt = NULL,
  format = c("vector", "list", "data.frame"),
  host = "http://localhost:1234",
  simplify = TRUE,
  quiet = FALSE,
  ...
)

Arguments

Value

The return type depends on the format argument:

• "vector": A character vector of responses. This format is only supported if simplify = TRUE and logprobs = FALSE.

• "list": A list where each element is the response corresponding to the provided input.

• "data.frame": A data.frame containing input and output columns. If logprobs = TRUE, an additional list-column named logprobs is included.

Chat Completion via Native API

Description

Direct interface to LM Studio's v1 Native endpoint. Optimized for stateful chats and hardware control.

Usage

lms_chat_native(
  model,
  input,
  system_prompt = NULL,
  host = "http://localhost:1234",
  simplify = TRUE,
  ...
)

Arguments

Value

If simplify = FALSE, returns a list representing the raw JSON response. If simplify = TRUE, returns a character string containing the model's text output.

Chat Completion via OpenAI Compatibility API

Description

Direct interface to LM Studio's OpenAI-compatible endpoint. Uses the messages array format.

Usage

lms_chat_openai(
  model,
  messages,
  host = "http://localhost:1234",
  logprobs = FALSE,
  simplify = TRUE,
  ...
)

Arguments

Value

If simplify = FALSE, returns a list representing the raw JSON response. Otherwise, returns a character string containing the generated text. If logprobs = TRUE, it returns an lms_chat_result object with the log probabilities populated as NULL since they are currently stubbed in the LM Studio OpenAI endpoint.

Chat Completion via OpenResponses API

Description

Direct interface to LM Studio's OpenResponses endpoint. Supports logprobs and custom instructions.

Usage

lms_chat_openresponses(
  model,
  input,
  instructions = NULL,
  host = "http://localhost:1234",
  logprobs = FALSE,
  simplify = TRUE,
  ...
)

Arguments

Value

If simplify = FALSE, returns a list representing the raw JSON response. Otherwise, returns a character string containing the generated text. If logprobs = TRUE, returns an object of class lms_chat_result incorporating both the text and probability data.

Start the LM Studio headless daemon

Description

Launches the llmster daemon in the background via the CLI. This is required in headless environments (such as Linux servers) before loading models or starting the local server.

Usage

lms_daemon_start()

Value

Invisibly returns the process object (or 0 if already running).

Desktop Users

On desktop operating systems (macOS and Windows), running this command may actually launch the LM Studio desktop application to act as the backend engine. If the GUI is already open, this function will simply detect the active instance and return successfully. While safe to use, desktop users generally do not need to call this function and can just open the application manually.

See Also

LM Studio Headless Daemon (llmster)

Examples

## Not run: 
lms_daemon_start()

## End(Not run)

Check the global status of LM Studio

Description

Displays the overall status of the LM Studio backend via the CLI, including loaded models and the server state. This function works regardless of whether the backend was started via the desktop GUI or the headless daemon.

Usage

lms_daemon_status()

Value

A character vector of the raw CLI output.

Examples

## Not run: 
lms_daemon_status()

## End(Not run)

Stop the LM Studio headless daemon

Description

Stops the llmster daemon via the CLI. Use this to clean up system resources when you are completely finished using LM Studio in headless mode.

Usage

lms_daemon_stop(force = FALSE)

Arguments

Value

Invisibly returns the system exit code (0 for success).

Desktop Users

If the daemon is currently being managed by the LM Studio desktop application, this function will fail. The CLI intentionally prevents programmatic shutdowns of the GUI to avoid disrupting visual sessions. In this scenario, you must close the desktop application manually.

Examples

## Not run: 
lms_daemon_stop(force = TRUE)

## End(Not run)

Download a model via REST API

Description

Download a model via REST API

Usage

lms_download(model, quantization = NULL, host = "http://localhost:1234", ...)

Arguments

Value

A character string containing the download job_id, or "already_downloaded" if already downloaded.

See Also

LM Studio Download Model API

Examples

## Not run: 
lms_server_start()

# Download a model by its HuggingFace identifier
job_id <- lms_download("google/gemma-3-1b")

# Download with a specific quantization level
lms_download("google/gemma-3-1b", quantization = "4bit")

## End(Not run)

Get the status of a download job

Description

Get the status of a download job

Usage

lms_download_status(job_id, host = "http://localhost:1234")

Arguments

Value

An object of class lms_download_status containing the download status.

See Also

LM Studio Download Status API

Examples

## Not run: 
lms_server_start()

job_id <- lms_download("google/gemma-3-1b")
status <- lms_download_status(job_id)
print(status)

## End(Not run)

Load a model via REST API

Description

Load a model via REST API

Usage

lms_load(
  model,
  context_length = NULL,
  eval_batch_size = NULL,
  flash_attention = NULL,
  num_experts = NULL,
  offload_kv_cache_to_gpu = NULL,
  echo_load_config = FALSE,
  force = FALSE,
  host = "http://localhost:1234",
  ...
)

Arguments

Value

Invisibly returns a character string of the loaded model identifier upon success. If echo_load_config = TRUE, it instead invisibly returns a list containing the model's detailed load configuration.

See Also

LM Studio Load Model API

Examples

## Not run: 
lms_server_start()
lms_download("google/gemma-3-1b")

# Load a model with default settings
lms_load("google/gemma-3-1b")

# Load a model with custom context length and flash attention enabled
lms_load("google/gemma-3-1b", context_length = 8192, flash_attention = TRUE)

## End(Not run)

Get the absolute path to the LMS executable

Description

Locates the LM Studio CLI (lms) on your system. It checks the RLMSTUDIO_LMS_PATH environment variable first, then the system PATH, and finally common installation directories.

Usage

lms_path()

Value

A character string specifying the absolute file path to the LM Studio executable (lms) on the user's system.

Examples

## Not run: 
lms_path()

## End(Not run)

Calculate Expected Scores and Uncertainty from Logprobs

Description

Takes a logprobs dataframe (from an lms_chat_result) and calculates the weighted average score, normalized probabilities, and uncertainty metrics.

Usage

lms_score_expected(lp_df, scale = 1:5)

Arguments

Value

A named list containing three numeric elements (expected_value, weighted_sd, entropy) and a data.frame named probabilities with columns label and prob. Returns NULL if the input dataframe is empty or invalid.

Examples

# Create a sample logprobs dataframe representing a model's generation step
mock_logprobs <- data.frame(
  step_token = rep("4", 3),
  step_logprob = rep(0, 3),
  candidate_token = c("4", "5", "3"),
  candidate_logprob = c(-0.105, -2.302, -3.506),
  stringsAsFactors = FALSE
)

# Calculate the expected score and uncertainty metrics
lms_score_expected(mock_logprobs, scale = 1:5)

Start the LM Studio local server

Description

Launches the LM Studio local server via the CLI, allowing you to interact with loaded models via HTTP API calls.

Usage

lms_server_start(port = NULL, cors = FALSE)

Arguments

Value

Invisibly returns an integer representing the system exit code (0 for success).

See Also

LM Studio CLI Server Start Documentation

Examples

## Not run: 
# Start server on the default port
lms_server_start()

# Start server on a custom port with CORS enabled
lms_server_start(port = 8080, cors = TRUE)

## End(Not run)

Check the status of the LM Studio server

Description

Displays the current status of the LM Studio local server via the CLI, including whether it is running and its configuration.

Usage

lms_server_status(
  json = FALSE,
  verbose = FALSE,
  quiet = FALSE,
  log_level = NULL
)

Arguments

Details

You can only use one logging control flag at a time (verbose, quiet, or log_level).

Value

By default, returns a character vector containing the raw CLI output. If json = TRUE and the jsonlite package is available, it returns a parsed list or data.frame of the status configuration.

See Also

LM Studio CLI Server Status Documentation

Examples

## Not run: 
lms_server_start()

# Get basic status string
lms_server_status()

# Get status as a parsed JSON data frame
lms_server_status(json = TRUE)

## End(Not run)

Stop the LM Studio local server

Description

Stops the currently running LM Studio local server via the CLI.

Usage

lms_server_stop()

Value

Invisibly returns an integer representing the system exit code (0 for success).

See Also

LM Studio CLI Server Stop Documentation

Examples

## Not run: 
lms_server_start()
lms_server_stop()

## End(Not run)

Unload a model from memory via REST API

Description

Unload a model from memory via REST API

Usage

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

Arguments

Value

Invisibly returns a character string representing the unloaded instance_id upon success.

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.

See Also

LM Studio Unload Model API

Examples

## Not run: 
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")

## End(Not run)

Unload all models from memory

Description

Retrieves a list of all currently loaded models and unloads them one by one.

Usage

lms_unload_all(host = "http://localhost:1234", ...)

Arguments

Value

Invisibly returns a character vector of the instance_ids that were successfully unloaded. If no models were currently loaded, it invisibly returns NULL.

See Also

lms_unload

Examples

## Not run: 
lms_server_start()
lms_download("google/gemma-3-1b")
lms_load("google/gemma-3-1b")

# Unload all currently loaded models to clear VRAM
lms_unload_all()

## End(Not run)

Create a new LM Studio chat result

Description

Internal constructor to create a structured object for responses containing log probabilities.

Usage

new_lms_chat_result(text = character(), logprobs = data.frame())

Arguments

Value

An object of class lms_chat_result.

Print an LM Studio chat result

Description

Custom print method for responses that include log probabilities. Displays the text clearly and provides a summary of the metadata.

Usage

## S3 method for class 'lms_chat_result'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Print method for LM Studio download status

Description

Print method for LM Studio download status

Usage

## S3 method for class 'lms_download_status'
print(x, ...)

Arguments

Value

Invisibly returns the input object x.

Examples

## Not run: 
lms_server_start()

job_id <- lms_download("google/gemma-3-1b")
status <- lms_download_status(job_id)
print(status)

## End(Not run)

Validate an LM Studio chat result

Description

Internal validator to ensure the integrity of lms_chat_result objects.

Usage

validate_lms_chat_result(x)

Arguments

Value

The validated object.

Run code with the LM Studio daemon active

Description

Temporarily starts the LM Studio headless daemon, executes the provided R expression, and then gracefully shuts the daemon and any active servers down. This is ideal for automated scripts and pipelines.

Usage

with_lms_daemon(code)

Arguments

Value

The result of the evaluated code.

Desktop Users

Be cautious using this wrapper if you already have the LM Studio GUI open. While the setup phase (lms_daemon_start) will succeed, the teardown phase (lms_daemon_stop) will fail because the CLI prevents programmatic shutdowns of the graphical interface. This wrapper is best reserved for strictly headless environments or fully automated scripts.

Examples

## Not run: 
result <- with_lms_daemon({
  lms_load("llama-3.1-8b")
  lms_chat("llama-3.1-8b", input = "Hello world!")
})

## End(Not run)