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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.
import argparse
from dataclasses import dataclass, field
from typing import Optional
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
from trl import GRPOConfig, GRPOTrainer, ModelConfig, ScriptArguments, TrlParser, get_peft_config
@dataclass
class GRPOScriptArguments(ScriptArguments):
"""
Script arguments for the GRPO training script.
Args:
reward_model_name_or_path (`str` or `None`):
Reward model id of a pretrained model hosted inside a model repo on huggingface.co or local path to a
directory containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`].
"""
reward_model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "Reward model id of a pretrained model hosted inside a model repo on huggingface.co or "
"local path to a directory containing model weights saved using `PreTrainedModel.save_pretrained`."
},
)
def main(script_args, training_args, model_args):
# Load a pretrained model
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
reward_model = AutoModelForSequenceClassification.from_pretrained(
script_args.reward_model_name_or_path, trust_remote_code=model_args.trust_remote_code, num_labels=1
)
# Load the dataset
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
# Initialize the GRPO trainer
trainer = GRPOTrainer(
model=model,
reward_funcs=reward_model,
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,
processing_class=tokenizer,
peft_config=get_peft_config(model_args),
)
# Train and push the model to the Hub
trainer.train()
# 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)
def make_parser(subparsers: argparse._SubParsersAction = None):
dataclass_types = (GRPOScriptArguments, GRPOConfig, ModelConfig)
if subparsers is not None:
parser = subparsers.add_parser("grpo", help="Run the GRPO training script", dataclass_types=dataclass_types)
else:
parser = TrlParser(dataclass_types)
return parser
if __name__ == "__main__":
parser = make_parser()
script_args, training_args, model_args = parser.parse_args_and_config()
main(script_args, training_args, model_args)
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