Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.

LLM AutoEval is a tool that simplifies the process of evaluating Large Language Models (LLMs) using a convenient Colab notebook. It automates the setup and execution of evaluations using RunPod, allowing users to customize evaluation parameters and generate summaries that can be uploaded to GitHub Gist for easy sharing and reference. LLM AutoEval supports various benchmark suites, including Nous, Lighteval, and Open LLM, enabling users to compare their results with existing models and leaderboards.

Hercules is the world’s first open-source testing agent designed to handle the toughest testing tasks for modern web applications. It turns simple Gherkin steps into fully automated end-to-end tests, making testing simple, reliable, and efficient. Hercules adapts to various platforms like Salesforce and is suitable for CI/CD pipelines. It aims to democratize and disrupt test automation, making top-tier testing accessible to everyone. The tool is transparent, reliable, and community-driven, empowering teams to deliver better software. Hercules offers multiple ways to get started, including using PyPI package, Docker, or building and running from source code. It supports various AI models, provides detailed installation and usage instructions, and integrates with Nuclei for security testing and WCAG for accessibility testing. The tool is production-ready, open core, and open source, with plans for enhanced LLM support, advanced tooling, improved DOM distillation, community contributions, extensive documentation, and a bounty program.

draive is an open-source Python library designed to simplify and accelerate the development of LLM-based applications. It offers abstract building blocks for connecting functionalities with large language models, flexible integration with various AI solutions, and a user-friendly framework for building scalable data processing pipelines. The library follows a function-oriented design, allowing users to represent complex programs as simple functions. It also provides tools for measuring and debugging functionalities, ensuring type safety and efficient asynchronous operations for modern Python apps.

Tara is a powerful node for ComfyUI that integrates Large Language Models (LLMs) to enhance and automate workflow processes. With Tara, you can create complex, intelligent workflows that refine and generate content, manage API keys, and seamlessly integrate various LLMs into your projects. It comprises nodes for handling OpenAI-compatible APIs, saving and loading API keys, composing multiple texts, and using predefined templates for OpenAI and Groq. Tara supports OpenAI and Grok models with plans to expand support to together.ai and Replicate. Users can install Tara via Git URL or ComfyUI Manager and utilize it for tasks like input guidance, saving and loading API keys, and generating text suitable for chaining in workflows.

This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for **fast prototyping** and **prompting** , and turning unstructured responses into **robust outputs** for various NLP tasks, **no training data** required. It supports open-source LLMs hosted on Hugging Face 🤗: Falcon, Dolly, Llama 2, OpenLLaMA, StableLM, Mistral. Integration with LangChain 🦜️🔗 - all `langchain` models and features can be used in `spacy-llm`. Tasks available out of the box: Named Entity Recognition, Text classification, Lemmatization, Relationship extraction, Sentiment analysis, Span categorization, Summarization, Entity linking, Translation, Raw prompt execution for maximum flexibility. Soon: Semantic role labeling. Easy implementation of **your own functions** via spaCy's registry for custom prompting, parsing and model integrations. For an example, see here. Map-reduce approach for splitting prompts too long for LLM's context window and fusing the results back together

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

FridonAI is an open-source project offering AI-powered tools for cryptocurrency analysis and blockchain operations. It includes modules like FridonAnalytics for price analysis, FridonSearch for technical indicators, FridonNotifier for custom alerts, FridonBlockchain for blockchain operations, and FridonChat as a unified chat interface. The platform empowers users to create custom AI chatbots, access crypto tools, and interact effortlessly through chat. The core functionality is modular, with plugins, tools, and utilities for easy extension and development. FridonAI implements a scoring system to assess user interactions and incentivize engagement. The application uses Redis extensively for communication and includes a Nest.js backend for system operations.

Chroma is an open-source embedding database that provides a simple, scalable, and feature-rich way to build Python or JavaScript LLM apps with memory. It offers a fully-typed, fully-tested, and fully-documented API that makes it easy to get started and scale your applications. Chroma also integrates with popular tools like LangChain and LlamaIndex, and supports a variety of embedding models, including Sentence Transformers, OpenAI embeddings, and Cohere embeddings. With Chroma, you can easily add documents to your database, query relevant documents with natural language, and compose documents into the context window of an LLM like GPT3 for additional summarization or analysis.

TypeChat.NET is a framework that provides cross-platform libraries for building natural language interfaces with language models using strong types, type validation, and simple type-safe programs. It translates user intent into strongly typed objects and JSON programs, with support for schema export, extensibility, and common scenarios. The framework is actively developed with frequent updates, evolving based on exploration and feedback. It consists of assemblies for translating user intent, synthesizing JSON programs, and integrating with Microsoft Semantic Kernel. TypeChat.NET requires familiarity with and access to OpenAI language models for its examples and scenarios.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

A comprehensive guide to building Retrieval Augmented Generation (RAG)-based LLM applications for production. This guide covers developing a RAG-based LLM application from scratch, scaling the major components, evaluating different configurations, implementing LLM hybrid routing, serving the application in a highly scalable and available manner, and sharing the impacts LLM applications have had on products.

HF-LLM.rs is a CLI tool for accessing Large Language Models (LLMs) like Llama 3.1, Mistral, Gemma 2, Cohere and more hosted on Hugging Face. It allows interaction with various models, providing input and receiving responses in a terminal environment. Users can select models, input prompts, receive streaming output, and engage in chat mode. The tool supports a variety of models available on Hugging Face infrastructure, with the list continuously updated. Some models may require a Pro subscription for access.

Discover Whisper-TikTok, an innovative AI-powered tool that leverages the prowess of Edge TTS, OpenAI-Whisper, and FFMPEG to craft captivating TikTok videos. Whisper-TikTok effortlessly generates accurate transcriptions from audio files and integrates Microsoft Edge Cloud Text-to-Speech API for vibrant voiceovers. The program orchestrates the synthesis of videos using a structured JSON dataset, generating mesmerizing TikTok content in minutes.

Crawlee-python is a web scraping and browser automation library that covers crawling and scraping end-to-end, helping users build reliable scrapers fast. It allows users to crawl the web for links, scrape data, and store it in machine-readable formats without worrying about technical details. With rich configuration options, users can customize almost any aspect of Crawlee to suit their project's needs.

Writing Tools is an Apple Intelligence-inspired application for Windows, Linux, and macOS that supercharges your writing with an AI LLM. It allows users to instantly proofread, optimize text, and summarize content from webpages, YouTube videos, documents, etc. The tool is privacy-focused, open-source, and supports multiple languages. It offers powerful features like grammar correction, content summarization, and LLM chat mode, making it a versatile writing assistant for various tasks.

Lhotse is a Python library designed to make speech and audio data preparation flexible and accessible. It aims to attract a wider community to speech processing tasks by providing a Python-centric design and an expressive command-line interface. Lhotse offers standard data preparation recipes, PyTorch Dataset classes for speech tasks, and efficient data preparation for model training with audio cuts. It supports data augmentation, feature extraction, and feature-space cut mixing. The tool extends Kaldi's data preparation recipes with seamless PyTorch integration, human-readable text manifests, and convenient Python classes.

Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.

Smile (Statistical Machine Intelligence and Learning Engine) is a comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. It covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc. Smile implements major machine learning algorithms and provides interactive shells for Java, Scala, and Kotlin. It supports model serialization, data visualization using SmilePlot and declarative approach, and offers a gallery showcasing various algorithms and visualizations.

RVC_CLI is a command line interface tool for retrieval-based voice conversion. It provides functionalities for installation, getting started, inference, training, UVR, additional features, and API integration. Users can perform tasks like single inference, batch inference, TTS inference, preprocess dataset, extract features, start training, generate index file, model extract, model information, model blender, launch TensorBoard, download models, audio analyzer, and prerequisites download. The tool is built on various projects like ContentVec, HIFIGAN, audio-slicer, python-audio-separator, RMVPE, FCPE, VITS, So-Vits-SVC, Harmonify, and others.

AICIty-reID 2020 is a repository containing the 1st Place submission to AICity Challenge 2020 re-id track by Baidu-UTS. It includes models trained on Paddlepaddle and Pytorch, with performance metrics and trained models provided. Users can extract features, perform camera and direction prediction, and access related repositories for drone-based building re-id, vehicle re-ID, person re-ID baseline, and person/vehicle generation. Citations are also provided for research purposes.

VideoTree is an official implementation for a query-adaptive and hierarchical framework for understanding long videos with LLMs. It dynamically extracts query-related information from input videos and builds a tree-based video representation for LLM reasoning. The tool requires Python 3.8 or above and leverages models like LaViLa and EVA-CLIP-8B for feature extraction. It also provides scripts for tasks like Adaptive Breath Expansion, Relevance-based Depth Expansion, and LLM Reasoning. The codebase is being updated to incorporate scripts/captions for NeXT-QA and IntentQA in the future.

MachineLearning is a repository focused on practical applications in various algorithm scenarios such as ship, education, and enterprise development. It covers a wide range of topics from basic machine learning and deep learning to object detection and the latest large models. The project utilizes mature third-party libraries, open-source pre-trained models, and the latest technologies from related papers to document the learning process and facilitate direct usage by a wider audience.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.