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"){'...
from safetensors.torch import load_file import torch import numpy as np from tqdm.auto import tqdm np.save("np_cache.npy", np.random.randn(400, 512, 512)) save_file({"data": torch.randn(400, 512, 512)}, "st_cache.safetensors") np_filename = "np_cache.npy" sf_filename = "...
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))} save_file(tensors,"new_model.safetensors") 1. 2. 3...
2.2.2保存模型权重 使用safetensors保存模型权重,而不是直接使用PyTorch的.save()方法。 代码语言:javascript 复制 importtorch from safetensors.torchimportsave_file # 假设model是你的模型实例 model_state_dict=model.state_dict()# 保存模型到safetensors格式save_file(model_state_dict,"model.safetensors") ...
使用safetensors保存模型权重,而不是直接使用PyTorch的.save()方法。 import torch from safetensors.torch import save_file # 假设model是你的模型实例 model_state_dict = model.state_dict() # 保存模型到safetensors格式 save_file(model_state_dict, "model.safetensors") 对应的pytorch保存模型的方法 #...
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 .torch import save_file, load_file, save, load to thesafetensors/__init__.pyfile in my conda environment's site_packages folder it seems to correspond to this path in the repo:https://github.com/huggingface/safetensors/tree/main/bindings/python/py_src/safetensors/__init__.py ...
System Info >>> safetensors.__version__ '0.4.2` On MacOS 14.2.1 Information The official example scripts My own modified scripts Reproduction >>> from safetensors.mlx import save_file >>> import mlx.core as mx >>> params = {"weight": mx...
importtorchfromsafetensorsimportsave_file, load_file# Create a tensortensor = torch.rand(3,3)# Save the tensorsave_file({"tensor": tensor},"tensor_data.safetensors")# Load the tensorloaded_tensors = load_file("tensor_data.safetensors") ...
import torch from safetensors import safe_open from safetensors.torch import save_file tensors = { "weight1": torch.zeros((1024, 1024)), "weight2": torch.zeros((1024, 1024)) } save_file(tensors, "model.safetensors") tensors = {} with safe_open("model.safetensors", framework="...