下面是利用 ChatGPT 生成的一个简化版本的代码,用于更加直观地理解上述过程。 import torch from transformers import BertTokenizer, BertForSequenceClassification, AdamW # 设置随机种子 seed_value = 42 torch.manual_seed(seed_value) torch.cuda.manual_seed_all(seed_value) # 如果使用GPU,则还需要设置这个 #...
如果你正在使用transformers库(这是pytorch_pretrained_bert的后续版本),你可以利用get_linear_schedule_with_warmup函数来方便地实现学习率的预热和衰减。例如: python from transformers import AdamW, get_linear_schedule_with_warmup optimizer = AdamW(model.parameters(), lr=base_lr) num_warmup_steps = len(...
I see old code from researcher on github use AdamW with huggingface scheduler from pytorch_transformers import AdamW, WarmupLinearSchedule Should I replace AdamW of huggingface to AdamW of pytorch ? from torch.optim import AdamW from pytorch_transformers import WarmupLinearSchedule Any advise ?
代码的分段运行结果,请参阅:Google Colab: BERT_Play.ipynb。 import torch from transformers import BertTokenizer, BertForPreTraining, AdamW # 设置随机种子 seed_value = 42 torch.manual_seed(seed_value) torch.cuda.manual_seed_all(seed_value) # 如果使用GPU,则还需要设置这个 # 初始化 tokenizer 和模...
tokenize import sent_tokenize from finbert.utils import * import numpy as np import logging from transformers.optimization import AdamW, get_linear_schedule_with_warmup from transformers import AutoTokenizer logger = logging.getLogger(__name__) @@ -41,7 +43,6 @@ def __init__(self, gradual_...
fromtransformersimportAutoTokenizer,AutoModelForCausalLM,TextStreamer importtorch model_name_or_path="TheBloke/OpenHermes-2.5-Mistral-7B-AWQ" tokenizer=AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") # model = AutoModelForCausalLM.from_pretrained( ...
from transformers import TrainingArguments from peft import LoraConfig from trl import RewardTrainer training_args = TrainingArguments( output_dir="./train_logs", max_steps=1000, per_device_train_batch_size=4, gradient_accumulation_steps=1, learning_rate=1.41e-5, optim="adamw_torch", save_...
optim="adamw_torch_fused", save_strategy="epoch", per_device_train_batch_size=8, per_device_eval_batch_size=8, weight_decay=0.01, save_total_limit=3, warmup_ratio=0.03, push_to_hub=False, report_to="none" ) trainer=Seq2SeqTrainer( ...
import torch.nn as nn from transformers import AutoModelForCausalLM, AutoTokenizer from torch.optim import AdamW from torch.utils.data import DataLoader # 1. 加载预训练模型和 tokenizer (例如, LLaMA-7B) model = AutoModelForCausalLM.from_pretrained("your_pretrained_model_path") ...
from transformers import PreTrainedModel, PreTrainedTokenizerBase from trl import SFTTrainer import torch import torch.nn.functional as F from .args import DistillArgs from ..common import get_current_device from ..datasets.loader import DatasetModule class FineTuning(SFTTrainer): def __init__(self...