2 个仓库
Awesome GitHub RepositoriesCross-Model Behavioral Testing
Tools for verifying that server responses remain consistent and correct across different AI model implementations.
Distinct from Model Testing: Distinct from Model Testing: focuses on the interaction between a server and multiple models rather than the internal performance of a single model.
Explore 2 awesome GitHub repositories matching testing & quality assurance · Cross-Model Behavioral Testing. Refine with filters or upvote what's useful.
Awesome Cross-Model Behavioral Testing GitHub Repositories
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- mcp-use/mcp-use
mcp-use/mcp-use
10,137在 GitHub 上查看mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents. The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protoco
Enables browser-based testing of server responses against multiple AI models to ensure consistent behavior.
TypeScriptagentic-frameworkaiapps-sdk - mrdbourke/zero-to-mastery-ml
mrdbourke/zero-to-mastery-ml
5,839在 GitHub 上查看本项目是一个机器学习教育课程和学习平台，通过交互式 Jupyter Notebooks 提供。它作为掌握 Python 数据科学工具包的综合指南，为数值计算、表格数据操作和统计可视化提供结构化教程。 该课程包括 Scikit-Learn 的具体实现指南，以及关于构建、训练和部署神经网络及计算机视觉模型的 TensorFlow 实践课程。它涵盖了构建预测模型的端到端过程，从初始问题定义和任务分类，到通过交互式 Web 界面部署模型。 该项目涵盖了广泛的功能领域，包括多维数组的数值计算、探索性数据分析和数据预处理例程。它为监督和无监督学习、自动化机器学习流水线、超参数优化以及使用分类指标和交叉验证的模型评估提供了详细的工作流。 教育内容组织为一系列 Notebook，将 Python 代码与叙述性解释交织在一起，以记录数据科学工作流。
Tests models across multiple different train-test splits to ensure performance is consistent and stable.
Jupyter Notebookdata-sciencedeep-learningmachine-learning
