Repository History

Lamini: The Official Python Client for Generative AI API

Lamini is the official Python client and SDK designed to interact with the Lamini API, enabling developers to create their own Generative AI applications. It provides a straightforward interface for integrating powerful AI capabilities into Python projects. This package simplifies the process of building and deploying generative AI solutions.

Zero: The AI Coding Agent for Your Local Terminal

Zero is an innovative AI coding agent designed for your local terminal, offering powerful capabilities to inspect repositories, edit files, run commands, and utilize browser/terminal helpers. It provides durable local sessions while giving users full control over the AI model and permission levels. This tool empowers developers with a customizable and secure AI assistant directly within their development environment.

xTuring: Build, Personalize, and Control Your Own LLMs

xTuring is an open-source framework designed to simplify the process of building, personalizing, and controlling Large Language Models (LLMs). It provides an easy way to fine-tune open-source LLMs on your own data, offering features from data pre-processing to efficient training and inference. This tool empowers developers to create private, personalized LLMs locally or in their private cloud environments.

RL4LMs: A Modular RL Library for Fine-tuning Language Models

RL4LMs is a powerful and modular reinforcement learning library designed to fine-tune language models to human preferences. It offers easily customizable building blocks for training, including on-policy algorithms, reward functions, and metrics. Thoroughly tested and benchmarked, RL4LMs supports a wide range of NLP tasks and models.

torchtune: PyTorch Native Library for LLM Post-Training and Experimentation

torchtune is a PyTorch native library designed for authoring, post-training, and experimenting with Large Language Models (LLMs). It offers hackable training recipes, simple PyTorch implementations of popular LLMs, and best-in-class memory efficiency. Please note: torchtune is no longer actively maintained as of 2025.

Griptape: Modular Python Framework for AI Agents and Workflows

Griptape is a modular Python framework designed to simplify the development of generative AI applications. It provides a flexible set of abstractions for working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and various other AI components. With its structured approach, Griptape enables developers to build sophisticated AI agents and workflows efficiently.

RouteLLM: Optimize LLM Costs and Maintain Quality with Intelligent Routing

RouteLLM is a powerful framework designed to serve and evaluate LLM routers, enabling significant cost savings without compromising response quality. It intelligently routes simpler queries to cheaper models while maintaining high performance, offering a drop-in replacement for existing OpenAI clients or a compatible server. This solution helps balance the dilemma of LLM deployment costs versus model capabilities.

Memoripy: An AI Memory Layer for Context-Aware Applications

Memoripy is a Python library designed to provide an AI memory layer for context-aware applications. It offers both short-term and long-term storage, semantic clustering, and optional memory decay. This robust tool helps AI systems manage and retrieve relevant information efficiently, supporting various LLM APIs like OpenAI and Ollama.

minimaxir-simpleaichat

RAGChecker: A Fine-grained Framework for Diagnosing RAG Systems

RAGChecker is an advanced automatic evaluation framework developed by Amazon Science, specifically designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It offers a comprehensive suite of metrics and tools for in-depth analysis of RAG performance. This framework empowers developers and researchers to thoroughly evaluate and enhance their RAG systems with precision.

rerankers: Unified API for Reranking and Cross-Encoder Models

rerankers is a lightweight, low-dependency Python library that provides a unified API for various reranking and cross-encoder models. It simplifies the integration of different reranking approaches into retrieval architectures, offering a consistent interface for diverse models like cross-encoders, RankGPT, T5, and API-based rerankers. This library aims to make reranking more accessible and easier to implement for developers.

LLM Compressor: Optimize LLMs for Deployment with vLLM

LLM Compressor is a Transformers-compatible Python library designed to apply various compression algorithms to Large Language Models (LLMs). It enables optimized deployment, especially with vLLM, by offering a comprehensive set of quantization techniques for weights, activations, and KV Cache. This tool seamlessly integrates with Hugging Face models, making LLM optimization accessible and efficient.