# PCT-V3 Training Config — QLoRA + BPE tokenizer
# Script: scripts/model_tiny.py

data:
  hf_repo: "AI-MO/NuminaMath-CoT"
  hf_split: "train"
  hf_num_eval: 50
  max_seq_length: 2048

training:
  output_dir: "outputs/tiny-qlora"
  run_name: "pct-v3-qlora"
  per_device_train_batch_size: 8
  per_device_eval_batch_size: 8
  gradient_accumulation_steps: 2
  max_grad_norm: 1.0
  num_train_epochs: 1
  max_steps: 50000
  learning_rate: 5.0e-4
  lr_scheduler_type: "cosine"
  warmup_ratio: 0.1
  weight_decay: 0.1
  use_cpu: false
  compile: false
  logging_steps: 5
  save_steps: 500
  seed: 42
  hf_repo_id: "samcheng0/lumia-tiny"

qlora:
  enabled: true
  r: 8
  alpha: 16
  dropout: 0.0

cft:
  enabled: true
  resume_checkpoint: "checkpoint.pt"
  reset_embeddings: true