save_steps=500, eval_steps=500, logging_steps=25, report_to=["tensorboard"], load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, ) 开始训练: from transformers import Seq2SeqTrainer ...
然后以较低的save_steps运行训练命令。deepspeed train_freeform.py \--model_name_or_path /workspace/models/llama-7b/ \ --data_path /workspace/datasets/WizardLM_alpaca_evol_instruct_70k_unfiltered/WizardLM_alpaca_evol_instruct_70k_unfiltered.json \--output_dir /workspace/models/WizardLM-7B-Uncens...
logging_steps=50, # save_strategy (default "steps"): # The checkpoint save strategy to adopt during training. Possible values are: # "no": No save is done during training. # "epoch": Save is done at the end of each epoch. # "steps": Save is done every save_steps (default 500)....
per_device_train_batch_size=1, per_device_eval_batch_size=1, weight_decay=0.01, logging_steps=10, push_to_hub=True, evaluation_strategy='steps', eval_steps=500, save_steps=1e6, gradient_accumulation_steps=16) 有一点与之前的设置不同,那就是新的参数,gradient_accumulation_steps。由于模型相当...
batch_size=2, predict_with_generate=True, logging_steps=2, # set to 1000 for full training save_steps=64, # set to 500 for full training eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # dele...
logging_steps=2,# set to 1000 for full trainingsave_steps=64,# set to 500 for full trainingeval_steps=64,# set to 8000 for full trainingwarmup_steps=1,# set to 2000 for full trainingmax_steps=128,# delete for full trainingoverwrite_output_dir=True, ...
# save_strategy (default "steps"): # The checkpoint save strategy to adopt during training. Possible values are: # "no": No save is done during training. # "epoch": Save is done at the end of each epoch. # "steps": Save is done every save_steps (default 500). ...
logging_strategy 和 logging_steps 每 50 个训练step保存日志(将由 TensorBoard 可视化)。 save_strategy 和 save_steps 表示每 200 个训练step保存训练模型。 learning_rate 学习率。per_device_train_batch_size 和 per_device_eval_batch_size 分别表示在训练和验证期间使用的批大小。 num_train_epochs表示训练...
save_steps: 训练期间,每 save_steps 步保存一次中间 checkpoint 并异步上传到 Hub。 eval_steps: 训练期间,每 eval_steps 步对中间 checkpoint 进行一次评估。 report_to: 训练日志的保存位置,支持 azure_ml、comet_ml、mlflow、neptune、tensorboard 以及wandb 这些平台。你可以按照自己的偏好进行选择,也可以直接使...
在这里,我们在每个历时结束时评估模型在验证集上的预测,调整权重衰减,并将save_steps设置为一个大数字,以禁用检查点,从而加快训练速度。 这也是确保我们登录到Hugging Face Hub的一个好时机(如果你在终端工作,你可以执行huggingface-cli login命令)。 from huggingface_hub import notebook_login notebook_login()...