Tag

LightRAG is an open-source framework designed for simple and fast Retrieval-Augmented Generation (RAG), integrating knowledge graphs, vector search, and efficient LLM-based processing to enhance question-answering over large document collections.

Claude Quickstarts is a collection of projects by Anthropic to help developers quickly build deployable applications using the Claude API. It includes quickstarts for a customer support agent, financial data analyst, computer use demo, and autonomous coding agent, demonstrating Claude's capabilities in natural language processing, data analysis, computer control, and automated coding.

A lightweight open-source platform for running, managing, and integrating large language models locally via a simple CLI and REST API.

Open-source framework that accelerates fine-tuning and full training of transformer LLMs by up to 30 × while cutting VRAM requirements by roughly 90 %, letting developers train custom models quickly on commodity GPUs.

Provides a consistent Python library of classic ML algorithms, preprocessing, model selection, and evaluation tools. Emphasizes a uniform estimator API, Pipelines, and tight NumPy/SciPy integration—suited for teaching and rapid prototyping of tabular and small-to-medium workloads.

NumPy is the fundamental package for scientific computing in Python. It provides a powerful N-dimensional array object, broadcasting operations, linear algebra, FFT, random number capabilities, and integration with C/C++/Fortran, and serves as a core building block for many AI and scientific libraries.

Provides APIs to build, learn, and run Bayesian and dynamic Bayesian networks, perform probabilistic inference, and compute interventional/counterfactual queries. Ships example notebooks, tutorials, and PyPI/conda packages. ([github.com](https://github.com/pgmpy/pgmpy))

High-performance, scalable gradient-boosted decision tree library for regression, classification, ranking and custom objectives. Multi-language bindings (Python, R, Java, Scala, C++), single-node, distributed and GPU training — widely used for tabular data and ML competitions.

Provides an open-source platform for building, training, and deploying machine learning models across CPU, GPU, and TPU. Includes high-level APIs, model hubs, and tools for production deployment and optimization.

Provides readable Python implementations and animated demos of core robotics algorithms (localization, SLAM, mapping, path planning, control). Minimal dependencies and a companion online textbook make it a practical teaching/reference repository for students and researchers.

High-performance gradient-boosting decision-tree library for training and serving tabular ML models; uses histogram-based learning, leaf-wise tree growth and GOSS for faster training, with Python/R bindings, distributed and GPU support.

Provides a Python-native, open-source deep learning framework with dynamic (eager) computation graphs, GPU acceleration, and a large ecosystem of libraries and pre-trained models — widely used for research and production. ([github.com](https://github.com/pytorch/pytorch?utm_source=openai))