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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 | # 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",
# "trackio",
# "kernels",
# ]
# ///
"""
Full training:
python examples/scripts/prm.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--dataset_name trl-lib/prm800k \
--output_dir Qwen2-0.5B-Reward \
--per_device_train_batch_size 8 \
--num_train_epochs 1 \
--learning_rate 1.0e-5 \
--eval_strategy steps \
--eval_steps 50
LoRA:
python examples/scripts/prm.py \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--dataset_name trl-lib/prm800k \
--output_dir Qwen2-0.5B-Reward-LoRA \
--per_device_train_batch_size 8 \
--num_train_epochs 1 \
--learning_rate 1.0e-4 \
--eval_strategy steps \
--eval_steps 50
--use_peft \
--lora_r 32 \
--lora_alpha 16
"""
import torch
from accelerate import logging
from datasets import load_dataset
from transformers import AutoModelForTokenClassification, AutoTokenizer, HfArgumentParser
from trl import (
ModelConfig,
ScriptArguments,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
from trl.experimental.prm import PRMConfig, PRMTrainer
logger = logging.get_logger(__name__)
if __name__ == "__main__":
parser = HfArgumentParser((ScriptArguments, PRMConfig, ModelConfig))
script_args, training_args, model_args = parser.parse_args_into_dataclasses()
################
# Model & Tokenizer
################
dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
model_kwargs = dict(
revision=model_args.model_revision,
use_cache=False if training_args.gradient_checkpointing else True,
)
quantization_config = get_quantization_config(model_args)
if quantization_config is not None:
# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["quantization_config"] = quantization_config
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, trust_remote_code=model_args.trust_remote_code, use_fast=True
)
model = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path, num_labels=2, trust_remote_code=model_args.trust_remote_code, **model_kwargs
)
if model_args.use_peft and model_args.lora_task_type != "TOKEN_CLS":
logger.warning(
"You are using a `task_type` that is different than `TOKEN_CLS` for PEFT. This will lead to silent bugs"
" Make sure to pass --lora_task_type TOKEN_CLS when using this script with PEFT.",
)
##############
# Load dataset
##############
dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
dataset = dataset.filter(lambda x: len(x["completions"]) > 0)
##########
# Training
##########
trainer = PRMTrainer(
model=model,
processing_class=tokenizer,
args=training_args,
train_dataset=dataset[script_args.dataset_train_split],
eval_dataset=dataset[script_args.dataset_test_split],
peft_config=get_peft_config(model_args),
)
trainer.train()
############################
# Save model and push to Hub
############################
trainer.save_model(training_args.output_dir)
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# 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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