save_pretrained(pt_save_directory) pt_model.save_pretrained(pt_save_directory) # Load the model pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained") # 支持加载不同架构(torch, tensorflow)学习的模型 tf_save_directory = "./tf_save_pretrained" tokenizer = AutoToken...
I'm having the same issue, i've fine tuned a Llama 7b model using peft, and got satisfying results in inference, but when i try to use SFTTrainer.save_model, and load the model from the saved files using LlamaForCausalLM.from_pretrained, the inference result seem to just be of the ...
有三种方式下载模型,一种是通过 huggingface model hub 的按钮下载,一种是使用 huggingface 的 transformers 库实例化模型进而将模型下载到缓存目录,另一种是通过 huggingface 的 huggingface_hub 工具进行下载。 huggingface 按钮下载 点击下图的下载按钮,把所有文件下载到一个目录即可。 transformers 实例化模型 import to...
I have defined my model via huggingface, but I don't know how to save and load the model, hopefully someone can help me out, thanks! CeyaoZhang and mertyyanik reacted with thumbs up emojiCeyaoZhang reacted with eyes emoji 👍
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[str]] = None,load_best_model_at_end: Optional[bool] = False,metric_for_best_model: Optional[str] = None,greater_is_better: Optional[bool] = None,ignore_data_skip: bool = False,sharded_ddp: str = '',deepspeed: Optional[str] = None,label_smoothing_factor: float = 0.0,adafactor: ...
我使用亚马逊SageMaker来训练HuggingFace模型。在提供给估计器的培训脚本的末尾,我将模型保存到正确的路径(SM_MODEL_DIR)中: parser = argparse.ArgumentParsertrainer.model.save_pretrained(args.model_dir) 在对模型进行了训练之后,我使用HuggingFace估计器的HuggingFace方法进行了部署。M ...
模型导出时将生成config.json和pytorch_model.bin参数文件。前者就是1中的配置文件,这和我们的直觉相同,即config和model应该是紧密联系在一起的两个类。后者其实和torch.save()存储得到的文件是相同的,这是因为Model都直接或者间接继承了Pytorch的Module类。从这里可以看出,HuggingFace在实现时很好地尊重了Pytorch的原生...
Model,也就是各种各样的模型。除了初始的Bert、GPT等基本模型,针对下游任务,还定义了诸如BertForQuestionAnswering等下游任务模型。模型导出时将生成config.json和pytorch_model.bin参数文件。前者就是1中的配置文件,这和我们的直觉相同,即config和model应该是紧密联系在一起的两个类。后者其实和torch.save()存储得到的...
model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python # implementation and a “Fast” implementation based on the Rust library Tokenizers. ...