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dee9fba | 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 | # Copyright 2024 The HuggingFace Inc. 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.
import os
from dataclasses import dataclass
from ..trainer.utils import OnPolicyConfig
@dataclass
class RLOOConfig(OnPolicyConfig):
r"""
Configuration class for the [`RLOOTrainer`].
Using [`~transformers.HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[: -len(".py")]`):
Name of this experiment.
reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`):
Path to the reward model.
num_ppo_epochs (`int`, *optional*, defaults to `4`):
Number of epochs to train.
whiten_rewards (`bool`, *optional*, defaults to `False`):
Whether to whiten the rewards.
kl_coef (`float`, *optional*, defaults to `0.05`):
KL coefficient.
cliprange (`float`, *optional*, defaults to `0.2`):
Clip range.
rloo_k (`int`, *optional*, defaults to `2`):
REINFORCE Leave-One-Out (RLOO) number of online samples per prompt.
"""
exp_name: str = os.path.basename(__file__)[: -len(".py")]
reward_model_path: str = "EleutherAI/pythia-160m"
num_ppo_epochs: int = 4
whiten_rewards: bool = False
kl_coef: float = 0.05
cliprange: float = 0.2
rloo_k: int = 2
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