Open Autonomy is a framework for creating agent services that run as a multi-agent-system and offer enhanced functionalities on-chain. It enables executing complex operations like machine-learning algorithms in a decentralized, trust-minimized, transparent, and robust manner.

The mini-swe-agent is a lightweight AI agent designed to solve GitHub issues and more. It is a minimal tool with just 100 lines of Python code, suitable for researchers, developers, and engineers looking for a simple, powerful, and deployable solution. The agent is built to be convenient, deployable in various environments, and tested for performance. It focuses on providing a hackable tool that is simple, convenient, and flexible, without unnecessary bloat or complexity. Users can run the agent with any model using bash, making it ideal for benchmarking, fine-tuning, and reinforcement learning tasks.

xLLM is an efficient LLM inference framework optimized for Chinese AI accelerators, enabling enterprise-grade deployment with enhanced efficiency and reduced cost. It adopts a service-engine decoupled inference architecture, achieving breakthrough efficiency through technologies like elastic scheduling, dynamic PD disaggregation, multi-stream parallel computing, graph fusion optimization, and global KV cache management. xLLM supports deployment of mainstream large models on Chinese AI accelerators, empowering enterprises in scenarios like intelligent customer service, risk control, supply chain optimization, ad recommendation, and more.

Denser Retriever is an enterprise-grade AI retriever designed to streamline AI integration into applications, combining keyword-based searches, vector databases, and machine learning rerankers using xgboost. It provides state-of-the-art accuracy on MTEB Retrieval benchmarking and supports various heterogeneous retrievers for end-to-end applications like chatbots and semantic search.

ClearML is an auto-magical suite of tools designed to streamline AI workflows. It includes modules for experiment management, MLOps/LLMOps, data management, model serving, and more. ClearML offers features like experiment tracking, model serving, orchestration, and automation. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm for remote debugging. ClearML aims to simplify collaboration, automate processes, and enhance visibility in AI projects.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

The serverless-rag-demo repository showcases a solution for building a Retrieval Augmented Generation (RAG) system using Amazon Opensearch Serverless Vector DB, Amazon Bedrock, Llama2 LLM, and Falcon LLM. The solution leverages generative AI powered by large language models to generate domain-specific text outputs by incorporating external data sources. Users can augment prompts with relevant context from documents within a knowledge library, enabling the creation of AI applications without managing vector database infrastructure. The repository provides detailed instructions on deploying the RAG-based solution, including prerequisites, architecture, and step-by-step deployment process using AWS Cloudshell.

MONAI is a PyTorch-based, open-source framework for deep learning in healthcare imaging. It provides a comprehensive set of tools for medical image analysis, including data preprocessing, model training, and evaluation. MONAI is designed to be flexible and easy to use, making it a valuable resource for researchers and developers in the field of medical imaging.

DeepChat is a versatile chat tool that supports multiple model cloud services and local model deployment. It offers multi-channel chat concurrency support, platform compatibility, complete Markdown rendering, and easy usability with a comprehensive guide. The tool aims to enhance chat experiences by leveraging various AI models and ensuring efficient conversation management.

MaxKB is a knowledge base Q&A system based on the LLM large language model. MaxKB = Max Knowledge Base, which aims to become the most powerful brain of the enterprise.

Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.

moai is a PyTorch-based AI Model Development Kit (MDK) designed to improve data-driven model workflows, design, and understanding. It offers modularity via monads for model building blocks, reproducibility via configuration-based design, productivity via a data-driven domain modelling language (DML), extensibility via plugins, and understanding via inter-model performance and design aggregation. The tool provides specific integrated actions like play, train, evaluate, plot, diff, and reprod to support heavy data-driven workflows with analytics, knowledge extraction, and reproduction. moai relies on PyTorch, Lightning, Hydra, TorchServe, ONNX, Visdom, HiPlot, Kornia, Albumentations, and the wider open-source community for its functionalities.

Axolotl is a lightweight and efficient tool for managing and analyzing large datasets. It provides a user-friendly interface for data manipulation, visualization, and statistical analysis. With Axolotl, users can easily import, clean, and explore data to gain valuable insights and make informed decisions. The tool supports various data formats and offers a wide range of functions for data processing and modeling. Whether you are a data scientist, researcher, or business analyst, Axolotl can help streamline your data workflows and enhance your data analysis capabilities.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

LLM Interface is an npm module that streamlines interactions with various Large Language Model (LLM) providers in Node.js applications. It offers a unified interface for switching between providers and models, supporting 36 providers and hundreds of models. Features include chat completion, streaming, error handling, extensibility, response caching, retries, JSON output, and repair. The package relies on npm packages like axios, @google/generative-ai, dotenv, jsonrepair, and loglevel. Installation is done via npm, and usage involves sending prompts to LLM providers. Tests can be run using npm test. Contributions are welcome under the MIT License.

Open Autonomy is a framework for creating agent services that run as a multi-agent-system and offer enhanced functionalities on-chain. It enables executing complex operations like machine-learning algorithms in a decentralized, trust-minimized, transparent, and robust manner.

LLM-Subtrans is an open source subtitle translator that utilizes LLMs as a translation service. It supports translating subtitles between any language pairs supported by the language model. The application offers multiple subtitle formats support through a pluggable system, including .srt, .ssa/.ass, and .vtt files. Users can choose to use the packaged release for easy usage or install from source for more control over the setup. The tool requires an active internet connection as subtitles are sent to translation service providers' servers for translation.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

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Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.