dragon_bundle: Package a run for a cloud GPU

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

Package a run for a cloud GPU

Description

Writes a run directory exactly as dragon_train() would, but instead of launching the trainer it zips everything a GPU machine needs: the data in chat format, the configuration, the trainer's Python code, and a notebook that runs it. Nothing is trained locally and Python is not needed.

Usage

dragon_bundle(
  dataset,
  model,
  lora = dragon_lora(),
  args = dragon_train_args(),
  name = NULL,
  run_dir = NULL,
  runs_dir = dragon_runs_dir(),
  n_samples = 10,
  revision = NULL,
  trust_remote_code = FALSE,
  method = NULL,
  beta = NULL,
  rewards = NULL,
  group_size = 4,
  temperature = 1,
  max_new_tokens = 128
)

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().
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.
method	For a dataset mapped with dragon_map_pairs(), the preference method, "dpo" or "orpo". Defaults to "dpo". Ignored for prompt and response data.
beta	Preference strength for method, or the KL weight for an RL run. Defaults to 0.1 and 0.04 respectively.
rewards	For a dataset mapped with dragon_map_prompts(), the dragon_reward() list an RL run needs.
group_size, temperature, max_new_tokens	RL sampling settings. See dragon_reinforce().

Details

Continue with dragon_remote() to open a provider and see the steps, and dragon_import() to bring the results back into this run directory.

Value

A dragon_run whose state is "bundled".

Examples

if (interactive()) {
run <- dragon_dataset(dragon_example_data()) |>
  dragon_map(prompt = "{subject}\n\n{body}", response = "reply") |>
  dragon_bundle("Qwen/Qwen2.5-0.5B-Instruct")
dragon_remote(run, "colab")
# ... train in the browser, download the results zip ...
dragon_import(run, "~/Downloads/dragonfarm-results-<run id>.zip")
dragon_generate(run, "My thermostat keeps dropping off Wi-Fi.")
}

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