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d0aa421e5e
* default learning rate * update trainer * update test * update script * update dataset format * add line in dpo doc * update orpo doc * refine implicit/explicit * update demo chat
114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Run the ORPO training script with the following command with some example arguments.
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In general, the optimal configuration for ORPO will be similar to that of DPO without the need for a reference model:
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# regular:
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python examples/scripts/orpo.py \
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--model_name_or_path=gpt2 \
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--per_device_train_batch_size 4 \
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--max_steps 1000 \
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--learning_rate 8e-6 \
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--gradient_accumulation_steps 1 \
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--logging_steps 10 \
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--eval_steps 500 \
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--output_dir="gpt2-aligned-orpo" \
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--warmup_steps 150 \
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--report_to wandb \
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--bf16 \
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--logging_first_step \
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--no_remove_unused_columns
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# peft:
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python examples/scripts/orpo.py \
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--model_name_or_path=gpt2 \
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--per_device_train_batch_size 4 \
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--max_steps 1000 \
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--learning_rate 8e-5 \
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--gradient_accumulation_steps 1 \
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--logging_steps 10 \
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--eval_steps 500 \
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--output_dir="gpt2-lora-aligned-orpo" \
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--optim rmsprop \
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--warmup_steps 150 \
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--report_to wandb \
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--bf16 \
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--logging_first_step \
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--no_remove_unused_columns \
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--use_peft \
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--lora_r=16 \
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--lora_alpha=16
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"""
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from dataclasses import dataclass, field
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
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from trl import ModelConfig, ORPOConfig, ORPOTrainer, get_peft_config
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from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
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@dataclass
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class ScriptArguments:
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dataset_name: str = field(
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default="trl-internal-testing/hh-rlhf-helpful-base-trl-style",
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metadata={"help": "The name of the dataset to use."},
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)
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if __name__ == "__main__":
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parser = HfArgumentParser((ScriptArguments, ORPOConfig, ModelConfig))
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script_args, training_args, model_config = parser.parse_args_into_dataclasses()
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################
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# Model & Tokenizer
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################
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model = AutoModelForCausalLM.from_pretrained(
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model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code
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)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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################
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# Dataset
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################
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dataset = load_dataset(script_args.dataset_name)
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if tokenizer.chat_template is None:
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tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
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################
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# Training
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################
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trainer = ORPOTrainer(
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model,
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args=training_args,
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train_dataset=dataset["train"],
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eval_dataset=dataset["test"],
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processing_class=tokenizer,
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peft_config=get_peft_config(model_config),
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)
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# train and save the model
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trainer.train()
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# Save and push to hub
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trainer.save_model(training_args.output_dir)
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if training_args.push_to_hub:
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trainer.push_to_hub(dataset_name=script_args.dataset_name)
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