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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.
"""
Usage:
python examples/scripts/xpo.py \
--model_name_or_path trl-lib/pythia-1b-deduped-tldr-sft \
--reward_model_path trl-lib/pythia-1b-deduped-tldr-rm \
--dataset_name trl-lib/tldr \
--learning_rate 5.0e-7 \
--output_dir pythia-1b-tldr-xpo \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 32 \
--num_train_epochs 3 \
--max_new_tokens 64 \
--warmup_ratio 0.1 \
--missing_eos_penalty 1.0 \
--push_to_hub
"""
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, GenerationConfig
from trl import (
HfPairwiseJudge,
LogCompletionsCallback,
ModelConfig,
OpenAIPairwiseJudge,
PairRMJudge,
ScriptArguments,
TrlParser,
XPOConfig,
XPOTrainer,
get_kbit_device_map,
get_quantization_config,
)
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
JUDGES = {"pair_rm": PairRMJudge, "openai": OpenAIPairwiseJudge, "hf": HfPairwiseJudge}
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, XPOConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_and_config()
training_args.gradient_checkpointing_kwargs = {"use_reentrant": True}
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,
attn_implementation=model_args.attn_implementation,
torch_dtype=torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
ref_model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
if training_args.reward_model_path is not None:
reward_model = AutoModelForSequenceClassification.from_pretrained(
training_args.reward_model_path,
num_labels=1,
trust_remote_code=model_args.trust_remote_code,
**model_kwargs,
)
else:
reward_model = None
if training_args.judge is not None:
judge_cls = JUDGES[training_args.judge]
judge = judge_cls()
else:
judge = None
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, padding_side="left", trust_remote_code=model_args.trust_remote_code
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.chat_template is None:
tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
trainer = XPOTrainer(
model=model,
ref_model=ref_model,
reward_model=reward_model,
judge=judge,
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,
)
if training_args.eval_strategy != "no":
generation_config = GenerationConfig(
max_new_tokens=training_args.max_new_tokens, do_sample=True, temperature=training_args.temperature
)
completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8)
trainer.add_callback(completions_callback)
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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