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Source channel @githubtrending · Post #15131 · Sep 9

#python#agent#llms AutoAgent lets you create and use powerful AI agents easily by just using natural language—no coding needed. It supports many large language models (LLMs) like OpenAI and Anthropic, and performs as well as top research AI systems on benchmarks. You can build tools, agents, and workflows quickly, manage data efficiently with its built-in vector database, and interact flexibly through different modes. It’s lightweight, customizable, and cost-effective, making it a personal AI assistant that helps automate complex tasks simply and efficiently. This saves you time and technical effort while giving you advanced AI capabilities. https://github.com/HKUDS/AutoAgent

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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