Working with an open-source Ollama model

In this tutorial, we will demonstrate how to use the quallmer package with an open-source Ollama model for qualitative coding tasks. Ollama provides a platform for running large language models (LLMs) locally on your machine, allowing you to leverage their capabilities without relying on cloud-based services.

We first install and run the Ollama app (outside of R) from https://ollama.com/download. Then, we install and load the rollama package in R and ping Ollama to ensure connectivity. We then download the model llama3.2:1b. Although it is a comparatively small LLM, this can take some time, depending on your machine.

# Do not forget to run the ollama app (outside of R) first!
# If you installed Ollama using the Windows/Mac installer, 
# you can simply start Ollama from your start menu/by clicking on the app icon.

# Install R package rollama 
#install.packages("rollama")

# Load the rollama package and ping Ollama to ensure connectivity.
library(rollama)
ping_ollama()
# if everything works as it should, 
# you should see the following in your console:
# Ollama (v0.4.2) is running at <http://localhost:11434>!

# install the light-weight model of Ollama, llama3.2:1b
#pull_model("llama3.2:1b")

# NOTE: llama3.2:1b is a comparatively small LLM 
# (only 1 billion parameters compared to over 1 trillion of gpt-o4)
# and might not be as powerful as other LLMs, 
# but it is a good starting point for educational purposes
# for more advanced tasks, you might want to use larger models
# which you can pull with the command pull_model("model_name")

# let's do a simple test with the model
query("What is the capital of Australia.", model = "llama3.2:1b")

Now that we have set up the Ollama model, we can use it with the quallmer package for qualitative coding tasks. To use an Ollama model, simply specify the model parameter in the qlm_code() function using the format "ollama/model". For example, to use the llama3.2:1b model, you would set model = "ollama/llama3.2:1b".