prm.yaml

base_model: Qwen/Qwen2.5-3B
# optionally might have model_type or tokenizer_type
model_type: AutoModelForTokenClassification
num_labels: 2
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

process_reward_model: true
chat_template:
datasets:
  - path: trl-lib/math_shepherd
    type: stepwise_supervised
    step_separator: "\n"
    max_completion_length:
    train_on_last_step_only: false

val_set_size: 0.2
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: 1
micro_batch_size: 8
eval_batch_size: 8
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16:
tf32:
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:
eval_steps: 100
saves_per_epoch: 1
weight_decay: 0.0
special_tokens: