ollamar

ollamar: An R package for running large language models - Published in JOSS (2025)

https://github.com/hauselin/ollama-r

Keywords

Repository

R library to run Ollama language models

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ollamar 


[![CRAN status](https://www.r-pkg.org/badges/version/ollamar)](https://CRAN.R-project.org/package=ollamar)
[![R-CMD-check](https://github.com/hauselin/ollama-r/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/hauselin/ollama-r/actions/workflows/R-CMD-check.yaml)
[![CRAN_Download_Badge](https://cranlogs.r-pkg.org/badges/grand-total/ollamar)](https://cran.r-project.org/package=ollamar)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.07211/status.svg)](https://doi.org/10.21105/joss.07211)


The [Ollama R library](https://hauselin.github.io/ollama-r/) is the easiest way to integrate R with [Ollama](https://ollama.com/), which lets you run language models locally on your own machine.

The library also makes it easy to work with data structures (e.g., conversational/chat histories) that are standard for different LLMs (such as those provided by OpenAI and Anthropic). It also lets you specify different output formats (e.g., dataframes, text/vector, lists) that best suit your need, allowing easy integration with other libraries/tools and parallelization via the `httr2` library.

To use this R library, ensure the [Ollama](https://ollama.com) app is installed. Ollama can use GPUs for accelerating LLM inference. See [Ollama GPU documentation](https://github.com/ollama/ollama/blob/main/docs/gpu.md) for more information.

See [Ollama's Github page](https://github.com/ollama/ollama) for more information. This library uses the [Ollama REST API (see documentation for details)](https://github.com/ollama/ollama/blob/main/docs/api.md) and was last tested on v0.5.4.

> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

## Ollama R vs Ollama Python/JS

This library has been inspired by the official [Ollama Python](https://github.com/ollama/ollama-python) and [Ollama JavaScript](https://github.com/ollama/ollama-js) libraries. If you're coming from Python or JavaScript, you should feel right at home. Alternatively, if you plan to use Ollama with Python or JavaScript, using this R library will help you understand the Python/JavaScript libraries as well.

## Installation

1. Download and install the [Ollama](https://ollama.com) app.

- [macOS](https://ollama.com/download/Ollama-darwin.zip)
- [Windows preview](https://ollama.com/download/OllamaSetup.exe)
- Linux: `curl -fsSL https://ollama.com/install.sh | sh`
- [Docker image](https://hub.docker.com/r/ollama/ollama)

2. Open/launch the Ollama app to start the local server.

3. Install either the stable or latest/development version of `ollamar`.

Stable version:

```{r eval=FALSE}
install.packages("ollamar")
```

For the latest/development version with more features/bug fixes (see latest changes [here](https://hauselin.github.io/ollama-r/news/index.html)), you can install it from GitHub using the `install_github` function from the `remotes` library. If it doesn't work or you don't have `remotes` library, please run `install.packages("remotes")` in R or RStudio before running the code below.

```{r eval=FALSE}
# install.packages("remotes")  # run this line if you don't have the remotes library
remotes::install_github("hauselin/ollamar")
```

## Example usage

Below is a basic demonstration of how to use the library. For details, see the [getting started vignette](https://hauselin.github.io/ollama-r/articles/ollamar.html) on our [main page](https://hauselin.github.io/ollama-r/).

`ollamar` uses the [`httr2` library](https://httr2.r-lib.org/index.html) to make HTTP requests to the Ollama server, so many functions in this library returns an `httr2_response` object by default. If the response object says `Status: 200 OK`, then the request was successful.

```{r eval=FALSE}
library(ollamar)

test_connection()  # test connection to Ollama server
# if you see "Ollama local server not running or wrong server," Ollama app/server isn't running

# download a model
pull("llama3.1")  # download a model (equivalent bash code: ollama run llama3.1)

# generate a response/text based on a prompt; returns an httr2 response by default
resp <- generate("llama3.1", "tell me a 5-word story")
resp

#' interpret httr2 response object
#' 
#' POST http://127.0.0.1:11434/api/generate  # endpoint
#' Status: 200 OK  # if successful, status code should be 200 OK
#' Content-Type: application/json
#' Body: In memory (414 bytes)

# get just the text from the response object
resp_process(resp, "text")
# get the text as a tibble dataframe
resp_process(resp, "df")

# alternatively, specify the output type when calling the function initially
txt <- generate("llama3.1", "tell me a 5-word story", output = "text")

# list available models (models you've pulled/downloaded)
list_models()
                        name    size parameter_size quantization_level            modified
1               codegemma:7b    5 GB             9B               Q4_0 2024-07-27T23:44:10
2            llama3.1:latest  4.7 GB           8.0B               Q4_0 2024-07-31T07:44:33
```

## Citing `ollamar`

If you use this library, please cite [this paper](https://joss.theoj.org/papers/10.21105/joss.07211) using the following BibTeX entry:

```bibtex
@article{Lin2025JOSS,
  author = {Lin, Hause and Safi, Tawab}, 
  title = {ollamar: An R package for running large language models}, 
  journal = {Journal of Open Source Software}, 
  volume = {10}, 
  number = {105}, 
  pages = {7211}, 
  year = {2025},
  month = jan,
  volume = {10}, 
  doi = {10.21105/joss.07211},
	url = {https://joss.theoj.org/papers/10.21105/joss.07211}
}
```

Owner

JOSS Publication

ollamar: An R package for running large language models

Published

January 24, 2025

DOI

10.21105/joss.07211

Volume 10, Issue 105, Page 7211

Authors

Hause Lin
Massachusetts Institute of Technology, USA

Tawab Safi
Massachusetts Institute of Technology, USA

Editor

Chris Vernon

Tags

large language models Ollama natural language processing artificial intelligence

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