dragon_pipeline: Run several post-training stages as one pipeline

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

Run several post-training stages as one pipeline

Description

Executes the steps in order, threading the result of each into the next: every training stage starts from the previous stage's run, synthesized pairs feed the next preference stage, and judge and evaluate steps score the latest run. Progress is written to ⁠pipelines/<id>.json⁠ under runs_dir after every step, so a pipeline can be watched from the app or another session with dragon_pipeline_status().

Usage

dragon_pipeline(
  model,
  steps,
  runs_dir = dragon_runs_dir(),
  name = "pipeline",
  background = FALSE
)

Arguments

model	Where the first training stage starts: a model id, a model directory, or a finished dragon_run.
steps	A list of dragon_step objects.
runs_dir	Where runs and the pipeline record are written.
name	Label used in the pipeline id.
background	Run in a separate R process.

Details

With background = TRUE the pipeline runs in a separate R process and the call returns at once. Steps must then be serializable: judges and teachers made with dragon_llm_anthropic() or plain functions are fine, ellmer chat objects are not.

Value

A dragon_pipeline object. In the foreground it holds the runs and every step's summary; in the background it is a handle whose progress dragon_pipeline_status() reads.

Examples

if (interactive()) {
p <- dragon_pipeline("Qwen/Qwen2.5-0.5B-Instruct", list(
  dragon_step_train(tickets),
  dragon_step_synthesize_pairs(judge = dragon_judge_anthropic(model = "claude-sonnet-5")),
  dragon_step_prefer(),
  dragon_step_judge(judge = dragon_judge_anthropic())
), background = TRUE)
dragon_pipeline_status(p)
}

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