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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Full training:
python examples/scripts/reward_modeling.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--dataset_name trl-lib/ultrafeedback_binarized \
--output_dir Qwen2-0.5B-Reward \
--per_device_train_batch_size 8 \
--num_train_epochs 1 \
--gradient_checkpointing True \
--learning_rate 1.0e-5 \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 50 \
--max_length 2048
LoRA:
python examples/scripts/reward_modeling.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--dataset_name trl-lib/ultrafeedback_binarized \
--output_dir Qwen2-0.5B-Reward-LoRA \
--per_device_train_batch_size 8 \
--num_train_epochs 1 \
--gradient_checkpointing True \
--learning_rate 1.0e-4 \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 50 \
--max_length 2048 \
--use_peft \
--lora_r 32 \
--lora_alpha 16
"""
import warnings
import torch
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
from trl import (
ModelConfig,
RewardConfig,
RewardTrainer,
ScriptArguments,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
setup_chat_format,
)
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, RewardConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
training_args.gradient_checkpointing_kwargs = dict(use_reentrant=False)
################
# Model & Tokenizer
################
torch_dtype = (
model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
)
quantization_config = get_quantization_config(model_args)
model_kwargs = dict(
revision=model_args.model_revision,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
use_cache=False if training_args.gradient_checkpointing else True,
torch_dtype=torch_dtype,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, use_fast=True
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_labels=1, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
# Align padding tokens between tokenizer and model
model.config.pad_token_id = tokenizer.pad_token_id
# If post-training a base model, use ChatML as the default template
if tokenizer.chat_template is None:
model, tokenizer = setup_chat_format(model, tokenizer)
if model_args.use_peft and model_args.lora_task_type != "SEQ_CLS":
warnings.warn(
"You are using a `task_type` that is different than `SEQ_CLS` for PEFT. This will lead to silent bugs"
" Make sure to pass --lora_task_type SEQ_CLS when using this script with PEFT.",
UserWarning,
)
##############
# Load dataset
##############
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
##########
# Training
##########
trainer = RewardTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split] if training_args.eval_strategy != "no" else None,
peft_config=get_peft_config(model_args),
)
trainer.train()
############################
# Save model and push to Hub
############################
trainer.save_model(training_args.output_dir)
if training_args.eval_strategy != "no":
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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