为此,我们将使用save_file函数。 importtorchfromsafetensors.torchimportsave_file,load_filetensors={"weight1":torch.zeros((1024,1024)),"weight2":torch.zeros((1024,1024))}save_file(tensors,"new_model.safetensors") 为了加载张量,我们将使用load_file函数。 load_file("new_model.safetensors"){'...
这里调用的许多函数都是safetensors库中的,如safe_open,safe_load_file等。这里首先调用metadata读取*.safetensors文件中的元数据,并查看该文件的格式,检查是否支持。若支持,则调用safe_load_file加载state_dict。 safe_load_file对应safetensors/torch.py/load_file函数,该函数的逻辑非常简单,对于每一个key,调用ge...
在成功导入safetensors.torch模块后,你可以查看该模块中是否包含load_file函数。这通常可以通过查看模块的__all__属性或使用dir()函数来实现,但最直接的方法是尝试导入该函数(如上述代码所示)。如果导入没有报错,说明load_file函数确实存在于safetensors.torch模块中。 了解load_file函数的参数和使用方法: load_fil...
复制 from safetensors.torchimportload_file # 加载模型权重 loaded_state_dict=load_file("model.safetensors")# 加载到模型中 model.load_state_dict(loaded_state_dict) 使用safetensors时,模型的加载和保存方式与直接使用PyTorch的.pt或.pth文件不同,它提供了额外的安全特性,特别是在模型的分发和共享方面 三...
在第二个示例中,我们将尝试保存使用torch.zeros创建的张量。为此,我们将使用save_file函数。 复制 importtorchfromsafetensors.torchimportsave_file,load_file tensors={"weight1": torch.zeros((1024,1024)),"weight2": torch.zeros((1024,1024))} ...
import torch from safetensors.torch import save_file, load_file tensors = { "weight1": torch.zeros((1024, 1024)), "weight2": torch.zeros((1024, 1024)) } save_file(tensors, "new_model.safetensors") And to load the tensors, we will use theload_filefunction. ...
from safetensors.torch import load_file # 加载模型权重 loaded_state_dict = load_file("model.safetensors") # 加载到模型中 model.load_state_dict(loaded_state_dict) 使用safetensors时,模型的加载和保存方式与直接使用PyTorch的.pt或.pth文件不同,它提供了额外的安全特性,特别是在模型的分发和共享方面...
Launch the TensorFlow Lite app on your device. Navigate through the app's interface until you find the "Load Model" option. From there, browse to the location where your safetensors file is stored and select it for loading.Once the model is loaded, you can utilize the ...
然后来看一下native层,把so文件load起的过程,先来一下nativeLoad()这个函数的实现(在JellyBean/dalvik/vm/native/java_lang_Runtime.cpp这个文件中): /* * static String nativeLoad(String filename, ClassLoader loader) * * Load the specified full path as a dynamic library filled with * JNI-compatible...
weights = load_file(sf_filename, device="cpu") load_time_st = datetime.datetime.now() - start_st st_load_time.append(load_time_st) start_pt = datetime.datetime.now() weights = torch.load(pt_filename, map_location="cpu") load_time_pt = datetime.datetime.now() - start_pt ...