File size: 3,488 Bytes
1fa3c6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | # Copyright 2020-2026 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[peft]",
# "trackio",
# "kernels",
# ]
# ///
"""
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:
# Full training:
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 \
--logging_first_step \
--no_remove_unused_columns
# QLoRA:
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 \
--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 ModelConfig, ScriptArguments, get_peft_config
from trl.experimental.cpo import CPOConfig, CPOTrainer
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)
################
# 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)
|