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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | # 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",
# ]
# ///
import argparse
import importlib
import os
import sys
from dataclasses import dataclass, field
from trl import ScriptArguments
@dataclass
class RLOOScriptArguments(ScriptArguments):
"""
Script arguments for the RLOO training script.
Args:
reward_model_name_or_path (`str`, *optional*):
Reward model id of a pretrained model hosted inside a model repo on huggingface.co or local path to a
directory containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`].
reward_funcs (`list[str]`, *optional*):
Reward functions to use. Supported values are:
- `"accuracy_reward"`
- `"reasoning_accuracy_reward"`
- `"think_format_reward"`
- `"get_soft_overlong_punishment"` (used value are `max_completion_len=1280`, `soft_punish_cache=256`)
- any dotted import path " (e.g., `'my_lib.rewards.custom_reward'`).
"""
reward_model_name_or_path: str | None = field(
default=None,
metadata={
"help": "Reward model id of a pretrained model hosted inside a model repo on huggingface.co or "
"local path to a directory containing model weights saved using `PreTrainedModel.save_pretrained`."
},
)
reward_funcs: list[str] | None = field(
default=None,
metadata={
"help": "Reward functions to use. Supported values are: `accuracy_reward`, `reasoning_accuracy_reward`, `think_format_reward`, "
"`get_soft_overlong_punishment` (used values are `max_completion_len=1280`, `soft_punish_cache=256`), or "
"any dotted import path (e.g., `'my_lib.rewards.custom_reward'`)."
},
)
def main(script_args, training_args, model_args, dataset_args):
from accelerate import logging
from datasets import load_dataset
from trl import RLOOTrainer, get_dataset, get_peft_config
from trl.rewards import (
accuracy_reward,
get_soft_overlong_punishment,
reasoning_accuracy_reward,
think_format_reward,
)
logger = logging.get_logger(__name__)
reward_funcs_registry = {
"accuracy_reward": accuracy_reward,
"reasoning_accuracy_reward": reasoning_accuracy_reward,
"think_format_reward": think_format_reward,
"get_soft_overlong_punishment": get_soft_overlong_punishment(max_completion_len=1280, soft_punish_cache=256),
}
# Get the reward models and functions
reward_funcs = []
if script_args.reward_model_name_or_path:
reward_funcs.append(script_args.reward_model_name_or_path)
if script_args.reward_funcs:
for func_name in script_args.reward_funcs:
if func_name in reward_funcs_registry:
reward_funcs.append(reward_funcs_registry[func_name])
elif "." in func_name:
module_path, func_name = func_name.rsplit(".", 1)
sys.path.insert(0, os.getcwd())
module = importlib.import_module(module_path)
reward_func = getattr(module, func_name)
reward_funcs.append(reward_func)
else:
raise ValueError(
f"Could not load reward function '{func_name}'. Expected one of "
f"{list(reward_funcs_registry.keys())} or a valid import path."
)
# 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 RLOO trainer
trainer = RLOOTrainer(
model=model_args.model_name_or_path,
reward_funcs=reward_funcs,
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, RLOOConfig, TrlParser
dataclass_types = (RLOOScriptArguments, RLOOConfig, ModelConfig, DatasetMixtureConfig)
if subparsers is not None:
parser = subparsers.add_parser("rloo", help="Run the RLOO 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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