Package a run for a cloud GPU — dragon

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().

Value

A dragon_run whose state is "bundled".

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.

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.")
}