File size: 4,054 Bytes
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # Copyright 2023 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.
from dataclasses import dataclass
from typing import Any, Dict, Optional
from transformers import TrainingArguments
@dataclass
class SFTConfig(TrainingArguments):
r"""
Configuration class for the [`SFTTrainer`].
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:
dataset_text_field (`str`, *optional*, defaults to `"text"`):
Name of the text field of the dataset. If provided, the trainer will automatically create a
[`ConstantLengthDataset`] based on `dataset_text_field`.
packing (`bool`, *optional*, defaults to `False`):
Controls whether the [`ConstantLengthDataset`] packs the sequences of the dataset.
learning_rate (`float`, *optional*, defaults to `2e-5`):
Initial learning rate for [`AdamW`] optimizer. The default value replaces that of [`~transformers.TrainingArguments`].
max_seq_length (`Optional[int]`, *optional*, defaults to `None`):
Maximum sequence length for the [`ConstantLengthDataset`] and for automatically creating the dataset. If
`None`, it uses the smaller value between `tokenizer.model_max_length` and `1024`.
dataset_num_proc (`Optional[int]`, *optional*, defaults to `None`):
Number of processes to use for processing the dataset. Only used when `packing=False`.
dataset_batch_size (`Union[int, None]`, *optional*, defaults to `1000`):
Number of examples to tokenize per batch. If `dataset_batch_size <= 0` or `dataset_batch_size is None`,
tokenizes the full dataset as a single batch.
model_init_kwargs (`Optional[Dict[str, Any]]`, *optional*, defaults to `None`):
Keyword arguments to pass to `AutoModelForCausalLM.from_pretrained` when instantiating the model from a
string.
dataset_kwargs (`Optional[Dict[str, Any]]`, *optional*, defaults to `None`):
Dictionary of optional keyword arguments to pass when creating packed or non-packed datasets.
eval_packing (`Optional[bool]`, *optional*, defaults to `None`):
Whether to pack the eval dataset. If `None`, uses the same value as `packing`.
num_of_sequences (`int`, *optional*, defaults to `1024`):
Number of sequences to use for the [`ConstantLengthDataset`].
chars_per_token (`float`, *optional*, defaults to `3.6`):
Number of characters per token to use for the [`ConstantLengthDataset`]. See
[chars_token_ratio](https://github.com/huggingface/trl/blob/08f550674c553c36c51d1027613c29f14f3676a5/examples/stack_llama/scripts/supervised_finetuning.py#L53) for more details.
use_liger (`bool`, *optional*, defaults to `False`):
Monkey patch the model with Liger kernels to increase throughput and reduce memory usage.
"""
dataset_text_field: str = "text"
packing: bool = False
learning_rate: float = 2.0e-5
max_seq_length: Optional[int] = None
dataset_num_proc: Optional[int] = None
dataset_batch_size: int = 1000
model_init_kwargs: Optional[Dict[str, Any]] = None
dataset_kwargs: Optional[Dict[str, Any]] = None
eval_packing: Optional[bool] = None
num_of_sequences: int = 1024
chars_per_token: float = 3.6
use_liger: bool = False
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