# DFlash speculative-decoding training recipe. Override fields via OmegaConf dotlist on the CLI.

# modelopt-schema: modelopt.recipe.config.ModelOptDFlashRecipe
metadata:
  description: DFlash training recipe (model/data/training/dflash bundled).

# maps to ModelArguments (main.py)
model:
  model_name_or_path:
  trust_remote_code: false
  use_fake_base_for_offline: false

# maps to DataArguments (main.py)
data:
  data_path:
  offline_data_path:
  # Jinja chat template with {% generation %} tags for answer_only_loss.
  # Required when answer_only_loss=true. Set in per-model launcher YAML.
  # Each model keeps its own beside its launcher example, e.g.
  # tools/launcher/examples/Qwen/Qwen3-8B/chat_template_train.jinja
  chat_template:

# maps to TrainingArguments (main.py)
training:
  # --- commonly modified ---
  output_dir:
  num_train_epochs: 10
  per_device_train_batch_size: 1
  learning_rate: 6.0e-4
  warmup_steps: 100
  training_seq_len: 4096
  logging_steps: 100
  save_steps: 5000
  cp_size: 1
  dp_shard_size: 1
  disable_tqdm: true
  estimate_ar: false
  ar_validate_steps: 0
  answer_only_loss: true

  # --- rarely modified ---
  do_eval: false
  lr_scheduler_type: linear
  save_strategy: steps
  weight_decay: 0.0
  dataloader_drop_last: true
  bf16: true
  tf32: true
  remove_unused_columns: false
  ddp_find_unused_parameters: true
  ddp_timeout: 1800
  report_to: tensorboard

# maps to DFlashConfig (modelopt/torch/speculative/config.py).
dflash:
  dflash_block_size: 8
  dflash_num_anchors: 512
  dflash_use_torch_compile: false
  dflash_self_logit_distillation: true
  dflash_loss_decay_factor: 4.0
  dflash_architecture_config:
    num_hidden_layers: 5
    # mask_token_id: auto-detected from model vocab (override for specific models)
    # sliding_window and layer_types are inherited from base model config automatically