DaoCloud Enterprise 5.0 Documentation provides detailed information on using DaoCloud, a Certified Kubernetes Service Provider. The documentation covers current and legacy versions, workflow control using GitOps, and instructions for opening a PR and previewing changes locally. It also includes naming conventions, writing tips, references, and acknowledgments to contributors. Users can find guidelines on writing, contributing, and translating pages, along with using tools like MkDocs, Docker, and Poetry for managing the documentation.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

Spandrel is a library for loading and running pre-trained PyTorch models. It automatically detects the model architecture and hyperparameters from model files, and provides a unified interface for running models.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

Ultralytics YOLO for Flutter is a Flutter plugin that allows you to integrate Ultralytics YOLO computer vision models into your mobile apps. It supports both Android and iOS platforms, providing APIs for object detection and image classification. The plugin leverages Flutter Platform Channels for seamless communication between the client and host, handling all processing natively. Before using the plugin, you need to export the required models in `.tflite` and `.mlmodel` formats. The plugin provides support for tasks like detection and classification, with specific instructions for Android and iOS platforms. It also includes features like camera preview and methods for object detection and image classification on images. Ultralytics YOLO thrives on community collaboration and offers different licensing paths for open-source and commercial use cases.

Vision Parse is a tool that leverages Vision Language Models to parse PDF documents into beautifully formatted markdown content. It offers smart content extraction, content formatting, multi-LLM support, PDF document support, and local model hosting using Ollama. Users can easily convert PDFs to markdown with high precision and preserve document hierarchy and styling. The tool supports multiple Vision LLM providers like OpenAI, LLama, and Gemini for accuracy and speed, making document processing efficient and effortless.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

r2ai is a tool designed to run a language model locally without internet access. It can be used to entertain users or assist in answering questions related to radare2 or reverse engineering. The tool allows users to prompt the language model, index large codebases, slurp file contents, embed the output of an r2 command, define different system-level assistant roles, set environment variables, and more. It is accessible as an r2lang-python plugin and can be scripted from various languages. Users can use different models, adjust query templates dynamically, load multiple models, and make them communicate with each other.

Factorio Learning Environment is an open source framework designed for developing and evaluating LLM agents in the game of Factorio. It provides two settings: Lab-play with structured tasks and Open-play for building large factories. Results show limitations in spatial reasoning and automation strategies. Agents interact with the environment through code synthesis, observation, action, and feedback. Tools are provided for game actions and state representation. Agents operate in episodes with observation, planning, and action execution. Tasks specify agent goals and are implemented in JSON files. The project structure includes directories for agents, environment, cluster, data, docs, eval, and more. A database is used for checkpointing agent steps. Benchmarks show performance metrics for different configurations.

Aim is a command-line tool for downloading and uploading files with resume support. It supports various protocols including HTTP, FTP, SFTP, SSH, and S3. Aim features an interactive mode for easy navigation and selection of files, as well as the ability to share folders over HTTP for easy access from other devices. Additionally, it offers customizable progress indicators and output formats, and can be integrated with other commands through piping. Aim can be installed via pre-built binaries or by compiling from source, and is also available as a Docker image for platform-independent usage.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

AQLM is the official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. It includes prequantized AQLM models without PV-Tuning and PV-Tuned models for LLaMA, Mistral, and Mixtral families. The repository provides inference examples, model details, and quantization setups. Users can run prequantized models using Google Colab examples, work with different model families, and install the necessary inference library. The repository also offers detailed instructions for quantization, fine-tuning, and model evaluation. AQLM quantization involves calibrating models for compression, and users can improve model accuracy through finetuning. Additionally, the repository includes information on preparing models for inference and contributing guidelines.

DaoCloud Enterprise 5.0 Documentation provides detailed information on using DaoCloud, a Certified Kubernetes Service Provider. The documentation covers current and legacy versions, workflow control using GitOps, and instructions for opening a PR and previewing changes locally. It also includes naming conventions, writing tips, references, and acknowledgments to contributors. Users can find guidelines on writing, contributing, and translating pages, along with using tools like MkDocs, Docker, and Poetry for managing the documentation.

GPT Home is a project that allows users to build their own home assistant using Raspberry Pi and OpenAI API. It serves as a guide for setting up a smart home assistant similar to Google Nest Hub or Amazon Alexa. The project integrates various components like OpenAI, Spotify, Philips Hue, and OpenWeatherMap to provide a personalized home assistant experience. Users can follow the detailed instructions provided to build their own version of the home assistant on Raspberry Pi, with optional components for customization. The project also includes system configurations, dependencies installation, and setup scripts for easy deployment. Overall, GPT Home offers a DIY solution for creating a smart home assistant using Raspberry Pi and OpenAI technology.

comfy-cli is a command line tool designed to simplify the installation and management of ComfyUI, an open-source machine learning framework. It allows users to easily set up ComfyUI, install packages, manage custom nodes, download checkpoints, and ensure cross-platform compatibility. The tool provides comprehensive documentation and examples to aid users in utilizing ComfyUI efficiently.

The crewAI Tools repository provides a guide for setting up tools for crewAI agents, enabling the creation of custom tools to enhance AI solutions. Tools play a crucial role in improving agent functionality. The guide explains how to equip agents with a range of tools and how to create new tools. Tools are designed to return strings for generating responses. There are two main methods for creating tools: subclassing BaseTool and using the tool decorator. Contributions to the toolset are encouraged, and the development setup includes steps for installing dependencies, activating the virtual environment, setting up pre-commit hooks, running tests, static type checking, packaging, and local installation. Enhance AI agent capabilities with advanced tooling.

Aipan-Netdisk-Search is a free and open-source web project for searching netdisk resources. It utilizes third-party APIs with IP access restrictions, suggesting self-deployment. The project can be easily deployed on Vercel and provides instructions for manual deployment. Users can clone the project, install dependencies, run it in the browser, and access it at localhost:3001. The project also includes documentation for deploying on personal servers using NUXT.JS. Additionally, there are options for donations and communication via WeChat.

Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.

Comfy-cli is a command line tool designed to facilitate the installation and management of ComfyUI, an open-source machine learning framework. Users can easily set up ComfyUI, install packages, and manage custom nodes directly from the terminal. The tool offers features such as easy installation, seamless package management, custom node management, checkpoint downloads, cross-platform compatibility, and comprehensive documentation. Comfy-cli simplifies the process of working with ComfyUI, making it convenient for users to handle various tasks related to the framework.

BentoDiffusion is a BentoML example project that demonstrates how to serve and deploy diffusion models in the Stable Diffusion (SD) family. These models are specialized in generating and manipulating images based on text prompts. The project provides a guide on using SDXL Turbo as an example, along with instructions on prerequisites, installing dependencies, running the BentoML service, and deploying to BentoCloud. Users can interact with the deployed service using Swagger UI or other methods. Additionally, the project offers the option to choose from various diffusion models available in the repository for deployment.

Flux All-In-One is a lightweight distribution optimized for running the GitOps Toolkit controllers as a single deployable unit on Kubernetes clusters. It is designed for bare clusters, edge clusters, clusters with restricted communication, clusters with egress via proxies, and serverless clusters. The distribution follows semver versioning and provides documentation for specifications, installation, upgrade, OCI sync configuration, Git sync configuration, and multi-tenancy configuration. Users can deploy Flux using Timoni CLI and a Timoni Bundle file, fine-tune installation options, sync from public Git repositories, bootstrap repositories, and uninstall Flux without affecting reconciled workloads.

Paddler is an open-source load balancer and reverse proxy designed specifically for optimizing servers running llama.cpp. It overcomes typical load balancing challenges by maintaining a stateful load balancer that is aware of each server's available slots, ensuring efficient request distribution. Paddler also supports dynamic addition or removal of servers, enabling integration with autoscaling tools.

DaoCloud Enterprise 5.0 Documentation provides detailed information on using DaoCloud, a Certified Kubernetes Service Provider. The documentation covers current and legacy versions, workflow control using GitOps, and instructions for opening a PR and previewing changes locally. It also includes naming conventions, writing tips, references, and acknowledgments to contributors. Users can find guidelines on writing, contributing, and translating pages, along with using tools like MkDocs, Docker, and Poetry for managing the documentation.

This repository contains a Docker image with ZeroTier One and ztncui to set up a standalone ZeroTier network controller with a web user interface. It provides features like Golang auto-mkworld for generating a planet file, supports local persistent storage configuration, and includes a public file server. Users can build the Docker image, set up the container with specific environment variables, and manage the ZeroTier network controller through the web interface.

DevOpsGPT is a revolutionary tool designed to streamline your workflow and empower you to build systems and automate tasks with ease. Tired of spending hours on repetitive DevOps tasks? DevOpsGPT is here to help! Whether you're setting up infrastructure, speeding up deployments, or tackling any other DevOps challenge, our app can make your life easier and more productive. With DevOpsGPT, you can expect faster task completion, simplified workflows, and increased efficiency. Ready to experience the DevOpsGPT difference? Visit our website, sign in or create an account, start exploring the features, and share your feedback to help us improve. DevOpsGPT will become an essential tool in your DevOps toolkit.

ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

AIOps Modules is a collection of reusable Infrastructure as Code (IAC) modules that work with SeedFarmer CLI. The modules are decoupled and can be aggregated using GitOps principles to achieve desired use cases, removing heavy lifting for end users. They must be generic for reuse in Machine Learning and Foundation Model Operations domain, adhering to SeedFarmer Guide structure. The repository includes deployment steps, project manifests, and various modules for SageMaker, Mlflow, FMOps/LLMOps, MWAA, Step Functions, EKS, and example use cases. It also supports Industry Data Framework (IDF) and Autonomous Driving Data Framework (ADDF) Modules.

The Fire-Flyer File System (3FS) is a high-performance distributed file system designed for AI training and inference workloads. It leverages modern SSDs and RDMA networks to provide a shared storage layer that simplifies development of distributed applications. Key features include performance, disaggregated architecture, strong consistency, file interfaces, data preparation, dataloaders, checkpointing, and KVCache for inference. The system is well-documented with design notes, setup guide, USRBIO API reference, and P specifications. Performance metrics include peak throughput, GraySort benchmark results, and KVCache optimization. The source code is available on GitHub for cloning and installation of dependencies. Users can build 3FS and run test clusters following the provided instructions. Issues can be reported on the GitHub repository.