model = TheModelClass(*args, **kwargs) • model.load_state_dict(torch.load(PATH)) • model.eval() • CONVENTION IS TO SAVE MODELS USING EITHER A .PT OR A .PTH EXTENSION https://pytorch.org/tutorials/beginn

训练 Qwen 的最简单方法。欢迎通过查看官方仓库深入了解详细信息！ 1.13 Function Calling 在 Qwen-Agent 中，我们提供了一个专用封装器，旨在实现通过 dashscope API 与 OpenAI API 进行的函数调 用。 1.13. Function Calling 37 Qwen 1.13.1 使用示例 import json import os from function response print('# Assistant Response 2:') for responses in llm.chat( (续下页) 1.13. Function Calling 39 Qwen (接上页) messages=messages, functions=functions, stream=True, ): # get a new response from

4-1 we manually assigned values for the cute and dangerous features for six animals2, and we are calling the tuple of these two features an embedding, where the two features are its dimensions. We will

given order until the data finally reaches the output layer. A ANN model can be created by simply calling Sequential() API as specified below: from keras.models import Sequential model = Sequential()

Networks,” 出处 Proceedings of the 34th International Conference on Machine Learning, International Convention Centre, Sydney, Australia, 2017. [6] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin 和 A.