Bert-VITS2 is a repository that provides a backbone with multilingual BERT for text-to-speech (TTS) applications. It offers an alternative to BV2/GSV projects and is inspired by the MassTTS project. Users can refer to the code to learn how to train models for TTS. The project is not maintained actively in the short term. It is not to be used for any purposes that violate the laws of the People's Republic of China, and strictly prohibits any political-related use.

Juggle is a low-code tool for interface orchestration, which can quickly orchestrate simple APIs into a complex interface. The orchestrated interface can be directly used by the front end, greatly improving development efficiency and reducing development costs.

AIBotPublic is an open-source version of AIBotPro, a comprehensive AI tool that provides various features such as knowledge base construction, AI drawing, API hosting, and more. It supports custom plugins and parallel processing of multiple files. The tool is built using bootstrap4 for the frontend, .NET6.0 for the backend, and utilizes technologies like SqlServer, Redis, and Milvus for database and vector database functionalities. It integrates third-party dependencies like Baidu AI OCR, Milvus C# SDK, Google Search, and more to enhance its capabilities.

Plug-play-modules is a comprehensive collection of plug-and-play modules for AI, deep learning, and computer vision applications. It includes various convolution variants, latest attention mechanisms, feature fusion modules, up-sampling/down-sampling modules, suitable for tasks like image classification, object detection, instance segmentation, semantic segmentation, single object tracking (SOT), multi-object tracking (MOT), infrared object tracking (RGBT), image de-raining, de-fogging, de-blurring, super-resolution, and more. The modules are designed to enhance model performance and feature extraction capabilities across various tasks.

Greasemonkey userscripts. A userscript manager such as Tampermonkey is required to run these scripts.

智元 Fast API is a one-stop API management system that unifies various LLM APIs in terms of format, standards, and management to achieve the ultimate in functionality, performance, and user experience. It includes features such as model management with intelligent and regex matching, backup model functionality, key management, proxy management, company management, user management, and chat management for both admin and user ends. The project supports cluster deployment, multi-site deployment, and cross-region deployment. It also provides a public API site for registration with a contact to the author for a 10 million quota. The tool offers a comprehensive dashboard, model management, application management, key management, and chat management functionalities for users.

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.

ChatGPT Auto-Continue is a userscript that automatically continues generating ChatGPT responses when chats cut off. It relies on the powerful chatgpt.js library and is easy to install and use. Simply install Tampermonkey and ChatGPT Auto-Continue, and visit chat.openai.com as normal. Multi-reply conversations will automatically continue generating when cut-off!

llama.cpp is a C++ implementation of LLaMA, a large language model from Meta. It provides a command-line interface for inference and can be used for a variety of tasks, including text generation, translation, and question answering. llama.cpp is highly optimized for performance and can be run on a variety of hardware, including CPUs, GPUs, and TPUs.

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. It provides a Plain C/C++ implementation without any dependencies, optimized for Apple silicon via ARM NEON, Accelerate and Metal frameworks, and supports various architectures like AVX, AVX2, AVX512, and AMX. It offers integer quantization for faster inference, custom CUDA kernels for NVIDIA GPUs, Vulkan and SYCL backend support, and CPU+GPU hybrid inference. llama.cpp is the main playground for developing new features for the ggml library, supporting various models and providing tools and infrastructure for LLM deployment.

GPT RSS is a tool that allows users to stay up-to-date on the latest AIGC/GPT/LLM articles by定时抓取前沿 AIGC / GPT / LLM 文章. It features a user-friendly interface that supports PC and mobile devices, as well as search and filter functions. GPT RSS is built using Vue3 and Vant UI component library, and utilizes Node.js for定时任务 to update articles daily.

ChatGPT Infinity is a free and powerful add-on that makes ChatGPT generate infinite answers on any topic. It offers customizable topic selection, multilingual support, adjustable response interval, and auto-scroll feature for a seamless chat experience.

NarratoAI is an automated video narration tool that provides an all-in-one solution for script writing, automated video editing, voice-over, and subtitle generation. It is powered by LLM to enhance efficient content creation. The tool aims to simplify the process of creating film commentary and editing videos by automating various tasks such as script writing and voice-over generation. NarratoAI offers a user-friendly interface for users to easily generate video scripts, edit videos, and customize video parameters. With future plans to optimize story generation processes and support additional large models, NarratoAI is a versatile tool for content creators looking to streamline their video production workflow.

Steel-LLM is a project to pre-train a large Chinese language model from scratch using over 1T of data to achieve a parameter size of around 1B, similar to TinyLlama. The project aims to share the entire process including data collection, data processing, pre-training framework selection, model design, and open-source all the code. The goal is to enable reproducibility of the work even with limited resources. The name 'Steel' is inspired by a band '万能青年旅店' and signifies the desire to create a strong model despite limited conditions. The project involves continuous data collection of various cultural elements, trivia, lyrics, niche literature, and personal secrets to train the LLM. The ultimate aim is to fill the model with diverse data and leave room for individual input, fostering collaboration among users.

X-AnyLabeling is a robust annotation tool that seamlessly incorporates an AI inference engine alongside an array of sophisticated features. Tailored for practical applications, it is committed to delivering comprehensive, industrial-grade solutions for image data engineers. This tool excels in swiftly and automatically executing annotations across diverse and intricate tasks.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

Bert-VITS2 is a repository that provides a backbone with multilingual BERT for text-to-speech (TTS) applications. It offers an alternative to BV2/GSV projects and is inspired by the MassTTS project. Users can refer to the code to learn how to train models for TTS. The project is not maintained actively in the short term. It is not to be used for any purposes that violate the laws of the People's Republic of China, and strictly prohibits any political-related use.

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

ModelFusion is an abstraction layer for integrating AI models into JavaScript and TypeScript applications, unifying the API for common operations such as text streaming, object generation, and tool usage. It provides features to support production environments, including observability hooks, logging, and automatic retries. You can use ModelFusion to build AI applications, chatbots, and agents. ModelFusion is a non-commercial open source project that is community-driven. You can use it with any supported provider. ModelFusion supports a wide range of models including text generation, image generation, vision, text-to-speech, speech-to-text, and embedding models. ModelFusion infers TypeScript types wherever possible and validates model responses. ModelFusion provides an observer framework and logging support. ModelFusion ensures seamless operation through automatic retries, throttling, and error handling mechanisms. ModelFusion is fully tree-shakeable, can be used in serverless environments, and only uses a minimal set of dependencies.

MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. It supports various languages including English (American, British, Indian, Australian), Spanish, French, Chinese, Japanese, and Korean. The Chinese speaker also supports mixed Chinese and English. The library is fast enough for CPU real-time inference and offers features like using without installation, local installation, and training on custom datasets. The Python API and model cards are available in the repository and on HuggingFace. The community can join the Discord channel for discussions and collaboration opportunities. Contributions are welcome, and the library is under the MIT License. MeloTTS is based on TTS, VITS, VITS2, and Bert-VITS2.

Call GPT is a voice application that utilizes Deepgram for Speech to Text, elevenlabs for Text to Speech, and OpenAI for GPT prompt completion. It allows users to chat with ChatGPT on the phone, providing better transcription, understanding, and speaking capabilities than traditional IVR systems. The app returns responses with low latency, allows user interruptions, maintains chat history, and enables GPT to call external tools. It coordinates data flow between Deepgram, OpenAI, ElevenLabs, and Twilio Media Streams, enhancing voice interactions.

OpenedAI Speech is a free, private text-to-speech server compatible with the OpenAI audio/speech API. It offers custom voice cloning and supports various models like tts-1 and tts-1-hd. Users can map their own piper voices and create custom cloned voices. The server provides multilingual support with XTTS voices and allows fixing incorrect sounds with regex. Recent changes include bug fixes, improved error handling, and updates for multilingual support. Installation can be done via Docker or manual setup, with usage instructions provided. Custom voices can be created using Piper or Coqui XTTS v2, with guidelines for preparing audio files. The tool is suitable for tasks like generating speech from text, creating custom voices, and multilingual text-to-speech applications.

MARS5 is a novel English speech model (TTS) developed by CAMB.AI, featuring a two-stage AR-NAR pipeline with a unique NAR component. The model can generate speech for various scenarios like sports commentary and anime with just 5 seconds of audio and a text snippet. It allows steering prosody using punctuation and capitalization in the transcript. Speaker identity is specified using an audio reference file, enabling 'deep clone' for improved quality. The model can be used via torch.hub or HuggingFace, supporting both shallow and deep cloning for inference. Checkpoints are provided for AR and NAR models, with hardware requirements of 750M+450M params on GPU. Contributions to improve model stability, performance, and reference audio selection are welcome.

The cgft-llm repository is a collection of video tutorials and documentation for implementing large models. It provides guidance on topics such as fine-tuning llama3 with llama-factory, lightweight deployment and quantization using llama.cpp, speech generation with ChatTTS, introduction to Ollama for large model deployment, deployment tools for vllm and paged attention, and implementing RAG with llama-index. Users can find detailed code documentation and video tutorials for each project in the repository.

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.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

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.