dragon_generate: Generate replies from a fine-tuned model

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

Generate replies from a fine-tuned model

Description

Runs through the session's inference backend: by default a local Python worker that keeps the last models loaded, so only the first call pays the load. Pass a server backend to generate from Ollama or any OpenAI-compatible endpoint instead. See dragon_backend.

Usage

dragon_generate(
  x,
  prompt,
  system = NULL,
  max_new_tokens = 256,
  temperature = 0.7,
  top_p = 0.9,
  base = FALSE,
  backend = dragon_backend()
)

Arguments

x	A dragon_run, a run directory, an adapter directory, a merged model directory, or a Hugging Face model id.
prompt	One or more user prompts.
system	Optional system prompt.
max_new_tokens	Maximum tokens to generate per reply.
temperature	Sampling temperature. 0 means greedy decoding.
top_p	Nucleus sampling threshold.
base	Ignore this run's adapter and generate from what it started with: the base model, or the earlier run it continued from. Useful for before-and-after comparisons.
backend	Where to run inference. See dragon_backend. Server backends serve a fixed model and ignore x.

Value

A character vector, one reply per prompt.

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