Quickstart: fine-tune a small model from R

1. Check the machine

library(dragonfarm)
dragon_check()

The first call builds a Python environment with torch and transformers. That is a 2 to 3 GB download and takes a few minutes. Later calls take a second. The report tells you which device training will use. A CPU is fine for the 135M and 360M models. Anything larger wants a GPU.

2. Load and map the data

A dataset is a table with one row per example. The bundled example has subject, body, product, and reply columns.

ds <- dragon_dataset(dragon_example_data())
ds

dragon_map() says which columns form the user turn and the assistant turn. Each argument is a column name or a template that combines columns.

ds <- dragon_map(ds,
  prompt = "{subject}\n\n{body}",
  response = "reply",
  system = "You are a support agent for a smart-home company. Be concrete and brief."
)
dragon_preview(ds, n = 1)

The system argument here is a constant. It could also be a column.

3. Train

run <- dragon_train(
  ds,
  model = "HuggingFaceTB/SmolLM2-135M-Instruct",
  lora = dragon_lora(r = 8, alpha = 16),
  args = dragon_train_args(epochs = 2, batch_size = 4, grad_accum = 2, max_seq_len = 512),
  wait = TRUE
)

With wait = TRUE you get a progress bar and the function returns when training ends. Without it, the function returns at once and you poll:

run <- dragon_train(ds, "HuggingFaceTB/SmolLM2-135M-Instruct")
dragon_status(run)$state
tail(dragon_progress(run))
dragon_logs(run, 10)
dragon_wait(run)

Runs are directories under dragonfarm_runs/. They survive the R session:

dragon_runs()
run <- dragon_run(dragon_runs()$dir[1])

4. Evaluate and try it

Training holds out 5 percent of rows and reports loss and perplexity on them, plus a few generated replies next to the reference replies.

ev <- dragon_evaluate(run)
ev
ev$samples

Compare the tuned model with the base model on a fresh prompt:

prompt <- "Charged twice for Sentry doorbell\n\nMy card shows two charges for one order."
dragon_generate(run, prompt, temperature = 0)
dragon_generate(run, prompt, temperature = 0, base = TRUE)

5. Ship it

The adapter alone is small and loads with peft. For a standalone model that needs neither peft nor dragonfarm, merge:

merged <- dragon_merge(run, "models/support-135m")
dragon_generate(merged, prompt)

To reproduce the run later, or share it, ask for the code:

cat(dragon_code(run))

Choosing settings

• Model. Start with HuggingFaceTB/SmolLM2-360M-Instruct or Qwen/Qwen2.5-0.5B-Instruct. Move up only if quality is not enough.
• Rank. 8 to 16 is plenty for format and tone. Go to 32 or 64 when the model must learn a lot of new facts.
• Learning rate. 2e-4 for LoRA. Halve it if the loss curve is jagged.
• Epochs. 2 to 3. Watch the eval loss: if it rises while train loss keeps falling, you are overfitting.
• Sequence length. Set it just above your longest example. Shorter is faster and uses less memory.
• Out of memory. Lower batch_size, raise grad_accum to compensate, and turn on gradient_checkpointing.