dragon_train_args: Training settings

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

Training settings

Description

Training settings

Usage

dragon_train_args(
  epochs = 3,
  learning_rate = 2e-04,
  batch_size = 4,
  grad_accum = 4,
  max_seq_len = 1024,
  max_steps = NULL,
  warmup_ratio = 0.03,
  weight_decay = 0,
  logging_steps = 5,
  save_steps = 100,
  gradient_checkpointing = FALSE,
  seed = 42,
  ...
)

Arguments

epochs	Passes over the training data. Ignored when max_steps is set.
learning_rate	Peak learning rate. 2e-4 is a good LoRA default.
batch_size	Examples per device per step.
grad_accum	Steps to accumulate before an optimizer update. Effective batch size is batch_size * grad_accum.
max_seq_len	Maximum tokens per example. Longer examples are truncated.
max_steps	Stop after this many optimizer steps. NULL trains for epochs.
warmup_ratio	Fraction of steps used to warm up the learning rate.
weight_decay	Weight decay for the optimizer.
logging_steps	Steps between progress rows.
save_steps	Steps between checkpoints.
gradient_checkpointing	Trade compute for memory. Turn on if you run out of GPU memory.
seed	Random seed.
...	Extra named arguments passed straight to TrainingArguments.

Value

A dragon_train_args object.

Examples

dragon_train_args(epochs = 1, learning_rate = 1e-4)

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