File size: 4,902 Bytes
2961be3 | 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 | # 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",
# ]
# ///
"""
# Full training
```
python trl/scripts/sft.py \
--model_name_or_path Qwen/Qwen2-0.5B \
--dataset_name trl-lib/Capybara \
--learning_rate 2.0e-5 \
--num_train_epochs 1 \
--packing \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--eos_token '<|im_end|>' \
--eval_strategy steps \
--eval_steps 100 \
--output_dir Qwen2-0.5B-SFT \
--push_to_hub
```
# LoRA
```
python trl/scripts/sft.py \
--model_name_or_path Qwen/Qwen2-0.5B \
--dataset_name trl-lib/Capybara \
--learning_rate 2.0e-4 \
--num_train_epochs 1 \
--packing \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 8 \
--eos_token '<|im_end|>' \
--eval_strategy steps \
--eval_steps 100 \
--use_peft \
--lora_r 32 \
--lora_alpha 16 \
--output_dir Qwen2-0.5B-SFT \
--push_to_hub
```
"""
import argparse
def main(script_args, training_args, model_args, dataset_args):
from accelerate.logging import get_logger
from datasets import load_dataset
from trl import SFTTrainer, get_dataset, get_kbit_device_map, get_peft_config, get_quantization_config
logger = get_logger(__name__)
training_args.model_init_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=training_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
dtype=model_args.dtype,
)
quantization_config = get_quantization_config(model_args)
if quantization_config is not None:
# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
training_args.model_init_kwargs["device_map"] = get_kbit_device_map()
training_args.model_init_kwargs["quantization_config"] = quantization_config
# 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 SFT trainer
trainer = SFTTrainer(
model=model_args.model_name_or_path,
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,
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, SFTConfig, TrlParser
dataclass_types = (ScriptArguments, SFTConfig, ModelConfig, DatasetMixtureConfig)
if subparsers is not None:
parser = subparsers.add_parser("sft", help="Run the SFT 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)
|