Abliteration is a tool that allows users to create abliterated models using transformers quickly and easily. It is not a tool for uncensorship, but rather for making models that will not explicitly refuse users. Users can clone the repository, install dependencies, and make abliterations using the provided commands. The tool supports adjusting parameters for stubborn models and offers various options for customization. Abliteration can be used for creating modified models for specific tasks or topics.

LMQL is a programming language designed for large language models (LLMs) that offers a unique way of integrating traditional programming with LLM interaction. It allows users to write programs that combine algorithmic logic with LLM calls, enabling model reasoning capabilities within the context of the program. LMQL provides features such as Python syntax integration, rich control-flow options, advanced decoding techniques, powerful constraints via logit masking, runtime optimization, sync and async API support, multi-model compatibility, and extensive applications like JSON decoding and interactive chat interfaces. The tool also offers library integration, flexible tooling, and output streaming options for easy model output handling.

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.

Vercel AI Provider for running Large Language Models locally using Ollama. This module is under development and may contain errors and frequent incompatible changes. It provides the capability of generating and streaming text and objects, with features like image input, object generation, tool usage simulation, tool streaming simulation, intercepting fetch requests, and provider management. The provider can be customized with optional settings like baseURL and headers.

This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.

easy-llama is a Python tool designed to make text generation using on-device large language models (LLMs) as easy as possible. It provides an abstraction layer over llama-cpp-python, simplifying the process of utilizing language models. The tool offers features such as automatic context length adjustment, terminal-based interactive chat, programmatic multi-turn interaction, support for various prompt formats, message-based context length handling, retrieval of likely next tokens, and compatibility with multiple models supported by llama-cpp-python. The upcoming version 0.2.0 will remove the llama-cpp-python dependency for improved efficiency and maintainability.

Ollama Operator is a Kubernetes operator designed to facilitate running large language models on Kubernetes clusters. It simplifies the process of deploying and managing multiple models on the same cluster, providing an easy-to-use interface for users. With support for various Kubernetes environments and seamless integration with Ollama models, APIs, and CLI, Ollama Operator streamlines the deployment and management of language models. By leveraging the capabilities of lama.cpp, Ollama Operator eliminates the need to worry about Python environments and CUDA drivers, making it a reliable tool for running large language models on Kubernetes.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

Woodpecker is a tool designed to correct hallucinations in Multimodal Large Language Models (MLLMs) by introducing a training-free method that picks out and corrects inconsistencies between generated text and image content. It consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Woodpecker can be easily integrated with different MLLMs and provides interpretable results by accessing intermediate outputs of the stages. The tool has shown significant improvements in accuracy over baseline models like MiniGPT-4 and mPLUG-Owl.

Scriberr is a self-hostable AI audio transcription app that utilizes open-source Whisper models from OpenAI for transcribing audio files locally on user's hardware. It offers fast transcription with customizable compute settings, local transcription on device, API endpoints for automation, and integration with other tools. Users can optionally summarize transcripts using ChatGPT or Ollama, with support for custom prompts. The app is mobile-ready, simple, and easy to use, with planned features including speaker diarization, audio recording, file actions, full text fuzzy search, tag-based organization, follow-along text with playback, edit summaries, export options, and support for other languages. Despite being in beta, Scriberr is functional and usable, albeit with some rough edges and minor bugs.

Agno is a lightweight library for building multi-modal Agents. It is designed with core principles of simplicity, uncompromising performance, and agnosticism, allowing users to create blazing fast agents with minimal memory footprint. Agno supports any model, any provider, and any modality, making it a versatile container for AGI. Users can build agents with lightning-fast agent creation, model agnostic capabilities, native support for text, image, audio, and video inputs and outputs, memory management, knowledge stores, structured outputs, and real-time monitoring. The library enables users to create autonomous programs that use language models to solve problems, improve responses, and achieve tasks with varying levels of agency and autonomy.

This minimalistic UI serves as a simple interface for Ollama models, enabling real-time interaction with Local Language Models (LLMs). Users can chat with models, switch between different LLMs, save conversations, and create parameter-driven prompt templates. The tool is built using React, Next.js, and Tailwind CSS, with seamless integration with LangchainJs and Ollama for efficient model switching and context storage.

RAG-FiT is a library designed to improve Language Models' ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library assists in creating training data, training models using parameter-efficient finetuning (PEFT), and evaluating performance using RAG-specific metrics. It is modular, customizable via configuration files, and facilitates fast prototyping and experimentation with various RAG settings and configurations.

IOPaint is a free and open-source inpainting & outpainting tool powered by SOTA AI model. It supports various AI models to perform erase, inpainting, or outpainting tasks. Users can remove unwanted objects, defects, watermarks, or people from images using erase models. Additionally, diffusion models can replace objects or perform outpainting. The tool also offers plugins for interactive object segmentation, background removal, anime segmentation, super resolution, face restoration, and file management. IOPaint provides a web UI for easy access to the latest AI models and supports batch processing of images through the command line. Developers can contribute to the project by installing front-end dependencies, setting up the backend, and starting the development environment for both front-end and back-end components.

Curated Transformers is a transformer library for PyTorch that provides state-of-the-art models composed of reusable components. It supports various transformer architectures, including encoders like ALBERT, BERT, and RoBERTa, and decoders like Falcon, Llama, and MPT. The library emphasizes consistent type annotations, minimal dependencies, and ease of use for education and research. It has been production-tested by Explosion and will be the default transformer implementation in spaCy 3.7.

MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities: * Emotional speech rhythm and tone in English. * Zero-shot cloning for American & British voices, with 30s reference audio. * Support for (cross-lingual) voice cloning with finetuning. * We have had success with as little as 1 minute training data for Indian speakers. * Synthesis of arbitrary length text

This repository contains an implementation of the Segment-Anything Model (SAM) within the SD WebUI. It allows users to divide layers in the SD WebUI and save them as PSD files. Users can adjust parameters, click 'Generate', and view the output below. A PSD file will be saved in the designated folder. The tool provides various parameters for customization, such as points_per_side, pred_iou_thresh, stability_score_thresh, crops_n_layers, crop_n_points_downscale_factor, and min_mask_region_area.

Abliteration is a tool that allows users to create abliterated models using transformers quickly and easily. It is not a tool for uncensorship, but rather for making models that will not explicitly refuse users. Users can clone the repository, install dependencies, and make abliterations using the provided commands. The tool supports adjusting parameters for stubborn models and offers various options for customization. Abliteration can be used for creating modified models for specific tasks or topics.

Cria is a Python library designed for running Large Language Models with minimal configuration. It provides an easy and concise way to interact with LLMs, offering advanced features such as custom models, streams, message history management, and running multiple models in parallel. Cria simplifies the process of using LLMs by providing a straightforward API that requires only a few lines of code to get started. It also handles model installation automatically, making it efficient and user-friendly for various natural language processing tasks.

Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.

Herc.ai is a powerful library for interacting with the Herc.ai API. It offers free access to users and supports all languages. Users can benefit from Herc.ai's features unlimitedly with a one-time subscription and API key. The tool provides functionalities for question answering and text-to-image generation, with support for various models and customization options. Herc.ai can be easily integrated into CLI, CommonJS, TypeScript, and supports beta models for advanced usage. Developed by FiveSoBes and Luppux Development.

New API is an open-source project based on One API with additional features and improvements. It offers a new UI interface, supports Midjourney-Proxy(Plus) interface, online recharge functionality, model-based charging, channel weight randomization, data dashboard, token-controlled models, Telegram authorization login, Suno API support, Rerank model integration, and various third-party models. Users can customize models, retry channels, and configure caching settings. The deployment can be done using Docker with SQLite or MySQL databases. The project provides documentation for Midjourney and Suno interfaces, and it is suitable for AI enthusiasts and developers looking to enhance AI capabilities.

LightRAG is a PyTorch library designed for building and optimizing Retriever-Agent-Generator (RAG) pipelines. It follows principles of simplicity, quality, and optimization, offering developers maximum customizability with minimal abstraction. The library includes components for model interaction, output parsing, and structured data generation. LightRAG facilitates tasks like providing explanations and examples for concepts through a question-answering pipeline.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

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.