File size: 3,557 Bytes
29b0094 | 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 | # Copyright 2023 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.
"""
# Full training
python examples/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 \
--gradient_checkpointing \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 100 \
--output_dir Qwen2-0.5B-SFT \
--push_to_hub
# LoRA
python examples/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 \
--gradient_checkpointing \
--logging_steps 25 \
--eval_strategy steps \
--eval_steps 100 \
--use_peft \
--lora_r 32 \
--lora_alpha 16 \
--output_dir Qwen2-0.5B-SFT \
--push_to_hub
"""
from datasets import load_dataset
from transformers import AutoTokenizer
from trl import (
ModelConfig,
ScriptArguments,
SFTConfig,
SFTTrainer,
TrlParser,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
if __name__ == "__main__":
parser = TrlParser((ScriptArguments, SFTConfig, ModelConfig))
script_args, training_args, model_config = parser.parse_args_and_config()
################
# Model init kwargs & Tokenizer
################
quantization_config = get_quantization_config(model_config)
model_kwargs = dict(
revision=model_config.model_revision,
trust_remote_code=model_config.trust_remote_code,
attn_implementation=model_config.attn_implementation,
torch_dtype=model_config.torch_dtype,
use_cache=False if training_args.gradient_checkpointing else True,
device_map=get_kbit_device_map() if quantization_config is not None else None,
quantization_config=quantization_config,
)
training_args.model_init_kwargs = model_kwargs
tokenizer = AutoTokenizer.from_pretrained(
model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, use_fast=True
)
tokenizer.pad_token = tokenizer.eos_token
################
# Dataset
################
dataset = load_dataset(script_args.dataset_name)
################
# Training
################
trainer = SFTTrainer(
model=model_config.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,
processing_class=tokenizer,
peft_config=get_peft_config(model_config),
)
trainer.train()
# Save and push to hub
trainer.save_model(training_args.output_dir)
if training_args.push_to_hub:
trainer.push_to_hub(dataset_name=script_args.dataset_name)
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