reward-model.yaml

base_model: google/gemma-2-2b
# optionally might have model_type or tokenizer_type
model_type: AutoModelForSequenceClassification
num_labels: 1
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

reward_model: true
chat_template: gemma
datasets:
  - path: argilla/distilabel-intel-orca-dpo-pairs
    type: bradley_terry.chat_template
val_set_size: 0.0
output_dir: ./outputs/out
remove_unused_columns: false

sequence_len: 2048
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch:
saves_per_epoch: 1
weight_decay: 0.0
special_tokens: