Popular GitHub Repositories for Pr Validation

structured outputs for llms

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Reliable LLM Memory for AI Applications and AI Agents

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

? Open-Source Evaluation & Testing library for LLM Agents

? Open-Source Evaluation & Testing for AI & LLM systems

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

A high-level machine learning and deep learning library for the PHP language.

Zero-boilerplate REST API framework for PHP 8+. Auto-generates routes, docs, validation.

Automatically find issues in image datasets and practice data-centric computer vision.

Perception toolkit for sim2real training and validation in Unity

Claude Code Skill for browser automation with Playwright. Model-invoked - Claude autonomously writes and executes custom automation for testing and validation.

Library for reading and processing ML training data.

? Autonomous AI development with 19 specialized sub-agents coordinated through Claude Code. Production-ready applications from natural language requests with browser validation.

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.

A full pipeline AutoML tool for tabular data