dragon_train: Fine-tune a model with LoRA

In dragonfarm: Fine-Tune Small Language Models with LoRA from R

Fine-tune a model with LoRA

Description

Writes a run directory, then launches the trainer as a background Python process. Returns immediately unless wait = TRUE. The run survives the R session; reopen it later with dragon_run().

Usage

dragon_train(
  dataset,
  model,
  lora = dragon_lora(),
  args = dragon_train_args(),
  hardware = dragon_hardware(),
  name = NULL,
  run_dir = NULL,
  runs_dir = dragon_runs_dir(),
  n_samples = 10,
  revision = NULL,
  trust_remote_code = FALSE,
  wait = FALSE
)

Arguments

dataset	A mapped dragon_dataset (see dragon_map()).
model	A Hugging Face model id such as "HuggingFaceTB/SmolLM2-135M-Instruct", a local model directory, or a finished dragon_run to continue from. In the last case the earlier run's adapters are folded into the weights before this run adds its own, so stages chain: fine-tune, then dragon_prefer(), and so on. See dragon_presets() for model ids.
lora	LoRA settings from dragon_lora().
args	Training settings from dragon_train_args().
hardware	Hardware settings from dragon_hardware().
name	Short label used in the run id. Defaults to the model name.
run_dir	Exact directory to use. Defaults to a timestamped directory under runs_dir.
runs_dir	Parent directory for runs. See dragon_runs_dir().
n_samples	Number of held-out rows to generate sample replies for at the end of training.
revision	Model revision (branch, tag, or commit) on the Hub.
trust_remote_code	Allow the model repository to run custom code.
wait	Block until training finishes.

Value

A dragon_run object.

Examples

if (interactive()) {
run <- dragon_dataset(dragon_example_data()) |>
  dragon_map(prompt = "{subject}\n\n{body}", response = "reply") |>
  dragon_train("HuggingFaceTB/SmolLM2-135M-Instruct", wait = TRUE)
dragon_generate(run, "My order arrived damaged.")
}

dragonfarm documentation built on Oct. 12, 2026, 5:07 p.m.