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Source channel @githubtrending · Post #14907 · Jul 3

#python#agents#generative_ai_tools#llamacpp#llm#onnx#openvino#parsing#retrieval_augmented_generation#small_specialized_models llmware is a powerful, easy-to-use platform that helps you build AI applications using small, specialized language models designed for business tasks like question-answering, summarization, and data extraction. It supports private, secure deployment on your own machines without needing expensive GPUs, making it cost-effective and safe for enterprise use. You can organize and search your documents, run smart queries, and combine knowledge with AI to get accurate answers quickly. It also offers many ready-to-use models and examples, plus tools for building chatbots and agents that automate complex workflows. This helps you save time, improve accuracy, and securely leverage AI for your business needs[1][3][5]. https://github.com/llmware-ai/llmware

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djangoproject

@djangoproject · Post #88 · 07/11/2016, 11:54 AM

https://docs.python.org/3/library/functools.html#functools.partialmethod class #functools.partialmethod(func, *args, **keywords) Return a new #partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable. func must be a descriptor or a callable (objects which are both, like normal functions, are handled as descriptors). When func is a descriptor (such as a normal Python function, classmethod(), staticmethod(), abstractmethod() or another instance of partialmethod), calls to __get__ are delegated to the underlying descriptor, and an appropriate partial object returned as the result. When func is a non-descriptor callable, an appropriate bound method is created dynamically. This behaves like a normal Python function when used as a method: the self argument will be inserted as the first positional argument, even before the args and keywords supplied to the partialmethod constructor.