我试图了解如何在本地保存微调的模型，而不是将其推向集线器。
我已经完成了一些教程，在微调的最后一步，模型正在运行 Trainer.train（）。然后，指令通常是： trainer.push_to_hub ，
但是如果我不想推到轮毂怎么办？我想在本地保存模型，然后可以将其从自己的计算机加载到将来的任务中，以便我可以进行推断而无需重新调整。
我该怎么做？
例如：最初从拥抱面上加载模型：
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.train()
以某种方式保存本地训练有素的新型号，以便下次我可以通过
model = 'some local directory where model and configs (?) got saved'
I'm trying to understand how to save a fine-tuned model locally, instead of pushing it to the hub.
I've done some tutorials and at the last step of fine-tuning a model is running trainer.train() . And then the instruction is usually: trainer.push_to_hub
But what if I don't want to push to the hub? I want to save the model locally, and then later be able to load it from my own computer into future task so I can do inference without re-tuning.
How can I do that?
eg: Initially load a model from hugging face:
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.train()
Somehow save the new trained model locally, so that next time I can pass
model = 'some local directory where model and configs (?) got saved'
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您可以使用：
或者，或者，或者，方法：
然后，在重新加载模型时，将保存的路径指定为：
You can use the
save_modelmethod:Or alternatively, the
save_pretrainedmethod:Then, when reloading your model, specify the path you saved to:
您可以使用
You can do it using