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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # 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 KTO training script with the commands below. In general, the optimal configuration for KTO will be similar to
that of DPO.
# Full training:
```bash
python trl/scripts/kto.py \
--dataset_name trl-lib/kto-mix-14k \
--model_name_or_path=trl-lib/qwen1.5-1.8b-sft \
--per_device_train_batch_size 16 \
--num_train_epochs 1 \
--learning_rate 5e-7 \
--lr_scheduler_type=cosine \
--gradient_accumulation_steps 1 \
--eval_steps 500 \
--output_dir=kto-aligned-model \
--warmup_steps 0.1 \
--logging_first_step
```
# QLoRA:
```bash
# QLoRA:
python trl/scripts/kto.py \
--dataset_name trl-lib/kto-mix-14k \
--model_name_or_path=trl-lib/qwen1.5-1.8b-sft \
--per_device_train_batch_size 8 \
--num_train_epochs 1 \
--learning_rate 5e-7 \
--lr_scheduler_type=cosine \
--gradient_accumulation_steps 1 \
--eval_steps 500 \
--output_dir=kto-aligned-model-lora \
--warmup_steps 0.1 \
--logging_first_step \
--use_peft \
--load_in_4bit \
--lora_target_modules=all-linear \
--lora_r=16 \
--lora_alpha=16
```
"""
import argparse
def main(script_args, training_args, model_args, dataset_args):
from accelerate import logging
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import get_dataset, get_peft_config
from trl.experimental.kto import KTOTrainer
logger = logging.get_logger(__name__)
# Load a pretrained model
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code
)
ref_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
# Load the dataset
if dataset_args.datasets and script_args.dataset_name:
logger.warning(
"Both `datasets` and `dataset_name` are provided. The `datasets` argument will be used to load the "
"dataset and `dataset_name` will be ignored."
)
dataset = get_dataset(dataset_args)
elif dataset_args.datasets and not script_args.dataset_name:
dataset = get_dataset(dataset_args)
elif not dataset_args.datasets and script_args.dataset_name:
dataset = load_dataset(
script_args.dataset_name, name=script_args.dataset_config, streaming=script_args.dataset_streaming
)
else:
raise ValueError("Either `datasets` or `dataset_name` must be provided.")
# Initialize the KTO trainer
trainer = KTOTrainer(
model,
ref_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 the model
trainer.train()
# Log training complete
trainer.accelerator.print("✅ Training completed.")
# Save and push to Hub
trainer.save_model(training_args.output_dir)
trainer.accelerator.print(f"💾 Model saved to {training_args.output_dir}.")
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
trainer.accelerator.print(f"🤗 Model pushed to the Hub in https://huggingface.co/{trainer.hub_model_id}.")
def make_parser(subparsers: argparse._SubParsersAction | None = None, prog: str | None = None):
from trl import DatasetMixtureConfig, ModelConfig, ScriptArguments, TrlParser
from trl.experimental.kto import KTOConfig
dataclass_types = (ScriptArguments, KTOConfig, ModelConfig, DatasetMixtureConfig)
if subparsers is not None:
parser = subparsers.add_parser("kto", help="Run the KTO training script", dataclass_types=dataclass_types)
else:
parser = TrlParser(dataclass_types, prog=prog)
return parser
if __name__ == "__main__":
parser = make_parser()
script_args, training_args, model_args, dataset_args = parser.parse_args_and_config(fail_with_unknown_args=False)
main(script_args, training_args, model_args, dataset_args)
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