RKLLM software stack is a toolkit designed to help users quickly deploy AI models to Rockchip chips. It consists of RKLLM-Toolkit for model conversion and quantization, RKLLM Runtime for deploying models on Rockchip NPU platform, and RKNPU kernel driver for hardware interaction. The toolkit supports RK3588 and RK3576 series chips and various models like TinyLLAMA, Qwen, Phi, ChatGLM3, Gemma, InternLM2, and MiniCPM. Users can download packages, docker images, examples, and docs from RKLLM_SDK. Additionally, RKNN-Toolkit2 SDK is available for deploying additional AI models.

This repository contains the training code of MobileLLM, a language model optimized for on-device use cases with fewer than a billion parameters. It integrates SwiGLU activation function, deep and thin architectures, embedding sharing, and grouped-query attention to achieve high-quality LLMs. MobileLLM-125M/350M shows significant accuracy improvements over previous models on zero-shot commonsense reasoning tasks. The design philosophy scales effectively to larger models, with state-of-the-art results for MobileLLM-600M/1B/1.5B.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. The purpose is to quickly replicate complete software environments showcasing the AI capabilities of Intel platforms. It includes optimizations for popular deep learning frameworks like TensorFlow and PyTorch, with additional plugins/extensions for improved performance. The repository is licensed under Apache License Version 2.0.

Kumo search is an end-to-end search engine framework that supports full-text search, inverted index, forward index, sorting, caching, hierarchical indexing, intervention system, feature collection, offline computation, storage system, and more. It runs on the EA (Elastic automic infrastructure architecture) platform, enabling engineering automation, service governance, real-time data, service degradation, and disaster recovery across multiple data centers and clusters. The framework aims to provide a ready-to-use search engine framework to help users quickly build their own search engines. Users can write business logic in Python using the AOT compiler in the project, which generates C++ code and binary dynamic libraries for rapid iteration of the search engine.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. It aims to replicate the best-known performance of target model/dataset combinations in optimally-configured hardware environments. The repository will be deprecated upon the publication of v3.2.0 and will no longer be maintained or published.

Here we will keep track of the AI Game Development Tools, including LLM, Agent, Code, Writer, Image, Texture, Shader, 3D Model, Animation, Video, Audio, Music, Singing Voice and Analytics. 🔥 * Tool (AI LLM) * Game (Agent) * Code * Framework * Writer * Image * Texture * Shader * 3D Model * Avatar * Animation * Video * Audio * Music * Singing Voice * Speech * Analytics * Video Tool

This repository contains an uncensored list of free to use APIs and sites for several AI models. > _This list is mainly managed by @zukixa, the queen of zukijourney, so any decisions may have bias!~_ > > **Scroll down for the sites, APIs come first!** * * * > [!WARNING] > We are not endorsing _any_ of the listed services! Some of them might be considered controversial. We are not responsible for any legal, technical or any other damage caused by using the listed services. Data is provided without warranty of any kind. **Use these at your own risk!** * * * # APIs Table of Contents #### Overview of Existing APIs #### Overview of Existing APIs -- Top LLM Models Available #### Overview of Existing APIs -- Top Image Models Available #### Overview of Existing APIs -- Top Other Features & Models Available #### Overview of Existing APIs -- Available Donator Perks * * * ## API List:* *: This list solely covers all providers I (@zukixa) was able to collect metrics in. Any mistakes are not my responsibility, as I am either banned, or not aware of x API. \ 1: Last Updated 4/14/24 ### Overview of APIs: | Service | # of Users1 | Link | Stablity | NSFW Ok? | Open Source? | Owner(s) | Other Notes | | ----------- | ---------- | ------------------------------------------ | ------------------------------------------ | --------------------------- | ------------------------------------------------------ | -------------------------- | ----------------------------------------------------------------------------------------------------------- | | zukijourney| 4441 | D | High | On /unf/, not /v1/ | ✅, Here | @zukixa | Largest & Oldest GPT-4 API still continuously around. Offers other popular AI-related Bots too. | | Hyzenberg| 1234 | D | High | Forbidden | ❌ | @thatlukinhasguy & @voidiii | Experimental sister API to Zukijourney. Successor to HentAI | | NagaAI | 2883 | D | High | Forbidden | ❌ | @zentixua | Honorary successor to ChimeraGPT, the largest API in history (15k users). | | WebRaftAI | 993 | D | High | Forbidden | ❌ | @ds_gamer | Largest API by model count. Provides a lot of service/hosting related stuff too. | | KrakenAI | 388 | D | High | Discouraged | ❌ | @paninico | It is an API of all time. | | ShuttleAI | 3585 | D | Medium | Generally Permitted | ❌ | @xtristan | Faked GPT-4 Before 1, 2 | | Mandrill | 931 | D | Medium | Enterprise-Tier-Only | ❌ | @fredipy | DALL-E-3 access pioneering API. Has some issues with speed & stability nowadays. | oxygen | 742 | D | Medium | Donator-Only | ❌ | @thesketchubuser | Bri'ish 🤮 & Fren'sh 🤮 | | Skailar | 399 | D | Medium | Forbidden | ❌ | @aquadraws | Service is the personification of the word 'feature creep'. Lots of things announced, not much operational. |

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

RAGHub is a community-driven project focused on cataloging new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. It aims to help users stay ahead of changes in the field by providing a platform for the latest innovations in RAG. The repository includes information on RAG frameworks, evaluation frameworks, optimization frameworks, citation frameworks, engines, search reranker frameworks, projects, resources, and real-world use cases across industries and professions.

LLM-Deploy focuses on the theory and practice of model/LLM reasoning and deployment, aiming to be your partner in mastering the art of LLM reasoning and deployment. Whether you are a newcomer to this field or a senior professional seeking to deepen your skills, you can find the key path to successfully deploy large language models here. The project covers reasoning and deployment theories, model and service optimization practices, and outputs from experienced engineers. It serves as a valuable resource for algorithm engineers and individuals interested in reasoning deployment.

The 'step_into_llm' repository is dedicated to the 昇思MindSpore technology open class, which focuses on exploring cutting-edge technologies, combining theory with practical applications, expert interpretations, open sharing, and empowering competitions. The repository contains course materials, including slides and code, for the ongoing second phase of the course. It covers various topics related to large language models (LLMs) such as Transformer, BERT, GPT, GPT2, and more. The course aims to guide developers interested in LLMs from theory to practical implementation, with a special emphasis on the development and application of large models.

ML-AI-2-LT is a repository that serves as a glossary for machine learning and deep learning concepts. It contains translations and explanations of various terms related to artificial intelligence, including definitions and notes. Users can contribute by filling issues for unclear concepts or by submitting pull requests with suggestions or additions. The repository aims to provide a comprehensive resource for understanding key terminology in the field of AI and machine learning.

This repository provides a list of the most starred and forked repositories on GitHub. It is updated automatically and includes information such as the project name, number of stars, number of forks, language, number of open issues, description, and last commit date. The repository is divided into two sections: LLM and chatGPT. The LLM section includes repositories related to large language models, while the chatGPT section includes repositories related to the chatGPT chatbot.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

sane-airscan is a SANE backend that supports driverless scanning using Apple AirScan (eSCL) and Microsoft WSD protocols. It automatically chooses between the two protocols and has been tested with various devices from Brother, Canon, Dell, Kyocera, Lexmark, Epson, HP, OKI, Panasonic, Pantum, Ricoh, Samsung, and Xerox. The backend allows for automatic and manual device discovery and configuration, supports scanning from platen and ADF in color and grayscale modes, and works with both IPv4 and IPv6. It does not require installation and does not conflict with vendor-provided proprietary software.

RKLLM software stack is a toolkit designed to help users quickly deploy AI models to Rockchip chips. It consists of RKLLM-Toolkit for model conversion and quantization, RKLLM Runtime for deploying models on Rockchip NPU platform, and RKNPU kernel driver for hardware interaction. The toolkit supports RK3588 and RK3576 series chips and various models like TinyLLAMA, Qwen, Phi, ChatGLM3, Gemma, InternLM2, and MiniCPM. Users can download packages, docker images, examples, and docs from RKLLM_SDK. Additionally, RKNN-Toolkit2 SDK is available for deploying additional AI models.

Ollama Colab Integration V4 is a tool designed to enhance the interaction and management of large language models. It allows users to quantize models within their notebook environment, access a variety of models through a user-friendly interface, and manage public endpoints efficiently. The tool also provides features like LiteLLM proxy control, model insights, and customizable model file templating. Users can troubleshoot model loading issues, CPU fallback strategies, and manage VRAM and RAM effectively. Additionally, the tool offers functionalities for downloading model files from Hugging Face, model conversion with high precision, model quantization using Q and Kquants, and securely uploading converted models to Hugging Face.

LLMinator is a Gradio-based tool with an integrated chatbot designed to locally run and test Language Model Models (LLMs) directly from HuggingFace. It provides an easy-to-use interface made with Gradio, LangChain, and Torch, offering features such as context-aware streaming chatbot, inbuilt code syntax highlighting, loading any LLM repo from HuggingFace, support for both CPU and CUDA modes, enabling LLM inference with llama.cpp, and model conversion capabilities.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

AI Edge Torch is a Python library that supports converting PyTorch models into a .tflite format for on-device applications on Android, iOS, and IoT devices. It offers broad CPU coverage with initial GPU and NPU support, closely integrating with PyTorch and providing good coverage of Core ATen operators. The library includes a PyTorch converter for model conversion and a Generative API for authoring mobile-optimized PyTorch Transformer models, enabling easy deployment of Large Language Models (LLMs) on mobile devices.

Bodhi App runs Open Source Large Language Models locally, exposing LLM inference capabilities as OpenAI API compatible REST APIs. It leverages llama.cpp for GGUF format models and huggingface.co ecosystem for model downloads. Users can run fine-tuned models for chat completions, create custom aliases, and convert Huggingface models to GGUF format. The CLI offers commands for environment configuration, model management, pulling files, serving API, and more.

lm.rs is a tool that allows users to run inference on Language Models locally on the CPU using Rust. It supports LLama3.2 1B and 3B models, with a WebUI also available. The tool provides benchmarks and download links for models and tokenizers, with recommendations for quantization options. Users can convert models from Google/Meta on huggingface using provided scripts. The tool can be compiled with cargo and run with various arguments for model weights, tokenizer, temperature, and more. Additionally, a backend for the WebUI can be compiled and run to connect via the web interface.

LiteRT is Google's open-source high-performance runtime for on-device AI, previously known as TensorFlow Lite. The repository is currently not intended for open-source development, but aims to evolve to allow direct building and contributions. LiteRT supports Python versions 3.9, 3.10, 3.11 on Linux and MacOS. It ensures compatibility with existing .tflite file extension and format, offering conversion tools and continued active development under the name LiteRT.

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.