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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.
# /// script
# dependencies = [
# "trl @ git+https://github.com/huggingface/trl.git",
# "peft",
# ]
# ///
"""
Run the CPO training script with the following command with some example arguments.
In general, the optimal configuration for CPO will be similar to that of DPO:
# regular:
python examples/scripts/cpo.py \
--dataset_name trl-lib/ultrafeedback_binarized \
--model_name_or_path=gpt2 \
--per_device_train_batch_size 4 \
--max_steps 1000 \
--learning_rate 8e-6 \
--gradient_accumulation_steps 1 \
--eval_steps 500 \
--output_dir="gpt2-aligned-cpo" \
--warmup_steps 150 \
--report_to wandb \
--bf16 \
--logging_first_step \
--no_remove_unused_columns
# peft:
python examples/scripts/cpo.py \
--dataset_name trl-lib/ultrafeedback_binarized \
--model_name_or_path=gpt2 \
--per_device_train_batch_size 4 \
--max_steps 1000 \
--learning_rate 8e-5 \
--gradient_accumulation_steps 1 \
--eval_steps 500 \
--output_dir="gpt2-lora-aligned-cpo" \
--optim rmsprop \
--warmup_steps 150 \
--report_to wandb \
--bf16 \
--logging_first_step \
--no_remove_unused_columns \
--use_peft \
--lora_r=16 \
--lora_alpha=16
"""
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
from trl import CPOConfig, CPOTrainer, ModelConfig, ScriptArguments, get_peft_config
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, CPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
################
# Model & Tokenizer
################
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
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
if tokenizer.chat_template is None:
tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
################
# Training
################
trainer = CPOTrainer(
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 save the model
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)
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