# Korean 3B SFT Configuration
#
# Base model: checkpoints/korean_3b_fp8_run1/checkpoint-XXXXXX (3B params pretrained)
# SFT 목표: instruction following + 반복 퇴화 완화 + 생성 품질 향상
# 아키텍처: LLaMA-3 3B 참고 (d=3072, 28L, 24H, GQA 8:1)
#
# 실행: bash scripts/launch_3b_sft.sh
#
# [설계 근거 — 2026-03-02]
#   - batch: 2 × 8GPU × 4 grad_accum = 64 eff_batch
#   - max_steps 33000 ≈ 3 epochs × 700K samples / 64 eff_batch
#   - lr=1e-5: pretrain 1.5e-4의 1/15 (catastrophic forgetting 방지)
#   - NEFTune alpha=5.0: 생성 다양성 향상, 반복 퇴화 완화
#   - use_fp8=true: B200 MXFP8 네이티브 가속 유지

model:
  vocab_size: 64000
  d_model: 3072
  n_layers: 28
  n_heads: 24
  n_kv_heads: 8
  d_ffn: 8192
  max_seq_len: 4096
  rope_theta: 500000.0
  dropout: 0.0
  bias: false
  use_flash_attn: true
  use_fp8: true

train:
  max_steps: 33000       # 3 epochs × 700K / 64 eff_batch
  batch_size: 2          # per GPU (3B VRAM 절약)
  grad_accum_steps: 4    # eff_batch: 2 × 8GPU × 4 = 64
  lr: 1.0e-5             # catastrophic forgetting 방지
  weight_decay: 0.01
  warmup_steps: 500
  max_grad_norm: 1.0
  log_interval: 10
  save_interval: 2000
  eval_interval: 500
  use_amp: false
  compile_model: false
  neftune_alpha: 5.0     # NEFTune noise injection

tokenizer:
  vocab_size: 64000
  type: sentencepiece_unigram