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# single node 8xh200
# torchrun --nproc-per-node=8 examples/llm_finetune/finetune.py --config examples/mixtral/mixtral-8x7b-v0-1_squad_peft.yaml

step_scheduler:
  global_batch_size: 32
  local_batch_size: 4
  ckpt_every_steps: 50
  val_every_steps: 1000
  max_steps: 100

dist_env:
  backend: nccl
  timeout_minutes: 1

rng:
  _target_: nemo_automodel.components.training.rng.StatefulRNG
  seed: 1111
  ranked: true

model:
  _target_: nemo_automodel.NeMoAutoModelForCausalLM.from_pretrained
  pretrained_model_name_or_path: mistralai/Mixtral-8x7B-Instruct-v0.1
  torch_dtype: bf16
  trust_remote_code: true

checkpoint:
  enabled: true
  checkpoint_dir: checkpoints/
  model_save_format: safetensors
  save_consolidated: false


peft:
  _target_: nemo_automodel.components._peft.lora.PeftConfig
  match_all_linear: True
  dim: 8
  alpha: 32
  use_triton: True


distributed:
  strategy: fsdp2
  dp_size: none
  tp_size: 4
  cp_size: 1
  pp_size: 1

  sequence_parallel: false

loss_fn:
  _target_: nemo_automodel.components.loss.masked_ce.MaskedCrossEntropy

# SQuAD instruction tuning dataset (5% train split for quick runs)
dataset:
  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
  dataset_name: rajpurkar/squad
  split: train

packed_sequence:
  packed_sequence_size: 0

# StatefulDataLoader with default collate
dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  collate_fn: nemo_automodel.components.datasets.utils.default_collater
  shuffle: true

validation_dataset:
  _target_: nemo_automodel.components.datasets.llm.squad.make_squad_dataset
  dataset_name: rajpurkar/squad
  split: validation

validation_dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  collate_fn: nemo_automodel.components.datasets.utils.default_collater

optimizer:
  _target_: torch.optim.Adam
  betas: [0.9, 0.999]
  eps: 1e-8
  lr: 1.0e-5
  weight_decay: 0

lr_scheduler:
  lr_decay_style: cosine
  min_lr: 1.0e-6 
