Write fine-tuning data with a teacher model — dragon

Sends each prompt to a stronger model and keeps its replies as the responses to train on. This is the fastest way to get good training data for a small model: a few hundred prompts from your domain, answered the way you want them answered. A quality pass then drops rows that are too short or too long, look garbled, repeat an earlier prompt, or repeat (or nearly repeat) an earlier response, so a teacher's stock phrases don't dominate the dataset. Passing a judge adds distillation with a quality gate: it scores every surviving reply and keeps only the ones at or above min_score, the way you would with a teacher answering a student's own prompts (see dragon_prompts()) and filtering out its weaker answers. The result is saved as JSONL and returned as a mapped dataset ready for dragon_train().

Usage

dragon_synthesize(
  prompts,
  teacher,
  system = NULL,
  max_new_tokens = 512,
  temperature = 0.7,
  judge = NULL,
  min_score = 7,
  rubric = NULL,
  dedupe = TRUE,
  near_dup_threshold = 0.92,
  min_chars = 1,
  max_chars = Inf,
  min_alpha_ratio = 0,
  file = NULL,
  runs_dir = dragon_runs_dir(),
  name = "synthetic"
)

Arguments

prompts: A character vector of prompts, or a mapped dragon_dataset whose prompt template is rendered for every row. See dragon_prompts() for prompts from an existing run, which is how distillation from "the model's own prompts" is done: pass dragon_prompts(run, "train").
teacher: The model that writes the replies: dragon_llm_anthropic(), an ellmer chat, a model id or directory, a finished run, or any function from prompts to replies.
system: System prompt for the teacher. Also stored with every row so the student trains with the same instruction.
max_new_tokens: Reply length cap for local teachers.
temperature: Sampling temperature for local teachers.
judge: Optional. Scores every surviving reply from 1 to 10 and drops the ones below min_score. See dragon_judge() for what is accepted.
min_score: Minimum judge score to keep a reply. Only used when judge is given.
rubric: What the judge should value. See dragon_judge().
dedupe: Drop rows whose prompt repeats an earlier one, and rows whose response exactly or nearly repeats an earlier response (see near_dup_threshold).
near_dup_threshold: Word-overlap similarity (0 to 1) above which two responses count as near-duplicates. Lower catches more; 1 disables near-duplicate detection while leaving exact-duplicate detection on.
min_chars, max_chars: Keep only replies whose length in characters falls in this range.
min_alpha_ratio: Minimum share of printable ASCII characters in a reply; a crude filter for garbled output or a reply in the wrong script. 0 (the default) disables it; non-English replies need it left off or set low.
file: Where to write the JSONL. Defaults to a timestamped file under synth/ in runs_dir.
runs_dir: Parent directory for the default file.
name: Label used in the default file name.

Value

A dragon_dataset mapped with prompt and response columns (and system, when given). The file path is its source, so dragon_code() reproduces runs trained on it. attr(ds, "synthesis")$dropped breaks down what the quality pass (and the judge, if used) removed.

Examples

if (interactive()) {
tickets <- dragon_dataset("tickets.csv") |>
  dragon_map(prompt = "{subject}\n\n{body}", response = "reply")
persona <- "You are a concise, warm support agent for a smart-home company."
teacher <- dragon_llm_anthropic(system = persona)
synth <- dragon_synthesize(tickets, teacher, system = persona)
run <- dragon_train(synth, "Qwen/Qwen2.5-0.5B-Instruct", wait = TRUE)

# Distill from the student's own prompts, keeping only replies a judge likes.
distilled <- dragon_synthesize(dragon_prompts(run, "train"), teacher,
                               judge = dragon_judge_anthropic(), min_score = 7)
}