sft_basic.yaml

# SFT Basic Example
# Fine-tune TinyLlama-1.1B on instruction-following data.
#
# The bundled dataset is a 10-row FORMAT sample, not a training set. This
# config runs end-to-end so you can verify your setup, but it will not
# produce a usefully fine-tuned model — point `data.train` at your own data
# for that. See examples/data/README.md.
#
# Usage:
#   soup train --config examples/configs/sft_basic.yaml

base: TinyLlama/TinyLlama-1.1B-Chat-v1.0
task: sft
backend: transformers

data:
  train: examples/data/alpaca_tiny.jsonl
  format: alpaca
  # 512: the longest row in any bundled fixture is ~233 tokens, so nothing is
  # truncated and there is 2x headroom. A larger value only inflates the
  # activation memory the VRAM pre-flight budgets for. Raise it when you point
  # this config at longer data of your own.
  max_length: 512

training:
  epochs: 1
  lr: 5e-4
  batch_size: 4
  gradient_accumulation_steps: 1
  quantization: none
  scheduler: linear
  warmup_ratio: 0.05
  weight_decay: 0.0
  logging_steps: 10
  save_steps: 50
  lora:
    r: 16
    alpha: 32
    dropout: 0.05
    target_modules:
      - q_proj
      - v_proj

output: ./output_sft_basic/