# SFT Training Config for QMD Query Expansion
# Target: Qwen3-1.7B with LoRA
#
# Usage: uv run train.py sft --config configs/sft.yaml

model:
  base: "Qwen/Qwen3-1.7B"
  output: "outputs/sft"  # Local training output (push to HF manually after eval)

dataset:
  # Local: run `uv run dataset/prepare_data.py` first, then use "data/train/"
  # HuggingFace: use "tobil/qmd-query-expansion-train" (already prepared)
  name: "data/train/"
  text_field: "text"
  split: "train"
  eval_split: 0.1

training:
  epochs: 5
  batch_size: 4
  gradient_accumulation_steps: 4
  learning_rate: 2e-4
  max_length: 512
  warmup_ratio: 0.03
  lr_scheduler: "cosine"
  # Save checkpoints every 30 minutes
  save_interval_minutes: 30
  # Fallback time-step save cadence if needed (not used for wall-clock mode)
  save_steps: 200
  save_total_limit: 3

lora:
  rank: 16
  alpha: 32
  dropout: 0.0
  target_modules:
    - "q_proj"
    - "k_proj"
    - "v_proj"
    - "o_proj"
    - "gate_proj"
    - "up_proj"
    - "down_proj"

tracking:
  project: "qmd-query-expansion"
  run_name: "sft-1.7B"