Popular GitHub Repositories for Core Set

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

BELLE: Be Everyone's Large Language model Engine（开源中文对话大模型）

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through our open source R&D automation tool RD-Agent, which lets AI drive data-driven AI.

Collection of models for Core ML

Mycroft Core, the Mycroft Artificial Intelligence platform.

AirLLM 70B inference with single 4GB GPU

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

A concise but complete full-attention transformer with a set of promising experimental features from various papers

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

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Power CLI and Workflow manager for LLMs (core package)

The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.

Core Engine of Singing Voice Conversion & Singing Voice Clone

A curated set of 200 plug-and-play n8n workflows that fuse classic automation with today’s AI stack—vector DBs, embeddings, and LLMs. Import any JSON, add your creds, hit Activate, and you’re live. Built to demo, prototype, or drop straight into production.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Set of use cases demonstrating how to build AI-featured applications.

A unified framework for privacy-preserving data analysis and machine learning