Free Chat is a forked project from chatgpt-demo that allows users to deploy a chat application with various features. It provides branches for different functionalities like token-based message list trimming and usage demonstration of 'promplate'. Users can control the website through environment variables, including setting OpenAI API key, temperature parameter, proxy, base URL, and more. The project welcomes contributions and acknowledges supporters. It is licensed under MIT by Muspi Merol.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

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.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

AIKit is a one-stop shop to quickly get started to host, deploy, build and fine-tune large language models (LLMs). AIKit offers two main capabilities: Inference: AIKit uses LocalAI, which supports a wide range of inference capabilities and formats. LocalAI provides a drop-in replacement REST API that is OpenAI API compatible, so you can use any OpenAI API compatible client, such as Kubectl AI, Chatbot-UI and many more, to send requests to open-source LLMs! Fine Tuning: AIKit offers an extensible fine tuning interface. It supports Unsloth for fast, memory efficient, and easy fine-tuning experience.

aiosmb is a fully asynchronous SMB library written in pure Python, supporting Python 3.7 and above. It offers various authentication methods such as Kerberos, NTLM, SSPI, and NEGOEX. The library supports connections over TCP and QUIC protocols, with proxy support for SOCKS4 and SOCKS5. Users can specify an SMB connection using a URL format, making it easier to authenticate and connect to SMB hosts. The project aims to implement DCERPC features, VSS mountpoint operations, and other enhancements in the future. It is inspired by Impacket and AzureADJoinedMachinePTC projects.

Large Language Models Play StarCraft II is a project that explores the capabilities of large language models (LLMs) in playing the game StarCraft II. The project introduces TextStarCraft II, a textual environment for the game, and a Chain of Summarization method for analyzing game information and making strategic decisions. Through experiments, the project demonstrates that LLM agents can defeat the built-in AI at a challenging difficulty level. The project provides benchmarks and a summarization approach to enhance strategic planning and interpretability in StarCraft II gameplay.

rwkv.cpp is a port of BlinkDL/RWKV-LM to ggerganov/ggml, supporting FP32, FP16, and quantized INT4, INT5, and INT8 inference. It focuses on CPU but also supports cuBLAS. The project provides a C library rwkv.h and a Python wrapper. RWKV is a large language model architecture with models like RWKV v5 and v6. It requires only state from the previous step for calculations, making it CPU-friendly on large context lengths. Users are advised to test all available formats for perplexity and latency on a representative dataset before serious use.

The ovos-installer is a simple and multilingual tool designed to install Open Voice OS and HiveMind using Bash, Whiptail, and Ansible. It supports various Linux distributions and provides an automated installation process. Users can easily start and stop services, update their Open Voice OS instance, and uninstall the tool if needed. The installer also allows for non-interactive installation through scenario files. It offers a user-friendly way to set up Open Voice OS on different systems.

Benchmark various LLM Structured Output frameworks like Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, LMFormatEnforcer, etc on tasks like multi-label classification, named entity recognition, synthetic data generation. The tool provides benchmark results, methodology, instructions to run the benchmark, add new data, and add a new framework. It also includes a roadmap for framework-related tasks, contribution guidelines, citation information, and feedback request.

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

optillm is an OpenAI API compatible optimizing inference proxy implementing state-of-the-art techniques to enhance accuracy and performance of LLMs, focusing on reasoning over coding, logical, and mathematical queries. By leveraging additional compute at inference time, it surpasses frontier models across diverse tasks.

The TiDB Documentation repository contains the source files for TiDB Docs in English and Chinese. Users can contribute by creating issues or pull requests to improve the documentation. It also provides guidance on customizing and generating PDF versions of the documentation. The repository maintains various versions of TiDB documentation in different branches, including development milestone releases and long-term support versions. Contributors can refer to the Contributing Guide to become a part of the project. The documentation is licensed under CC BY-SA 3.0.

Agentic is a standard AI functions/tools library optimized for TypeScript and LLM-based apps, compatible with major AI SDKs. It offers a set of thoroughly tested AI functions that can be used with favorite AI SDKs without writing glue code. The library includes various clients for services like Bing web search, calculator, Clearbit data resolution, Dexa podcast questions, and more. It also provides compound tools like SearchAndCrawl and supports multiple AI SDKs such as OpenAI, Vercel AI SDK, LangChain, LlamaIndex, Firebase Genkit, and Dexa Dexter. The goal is to create minimal clients with strongly-typed TypeScript DX, composable AIFunctions via AIFunctionSet, and compatibility with major TS AI SDKs.

Fitten Code AI Programming Assistant for Neovim provides fast completion using AI, asynchronous I/O, and support for various actions like document code, edit code, explain code, find bugs, generate unit test, implement features, optimize code, refactor code, start chat, and more. It offers features like accepting suggestions with Tab, accepting line with Ctrl + Down, accepting word with Ctrl + Right, undoing accepted text, automatic scrolling, and multiple HTTP/REST backends. It can run as a coc.nvim source or nvim-cmp source.

Ollama GUI is a web interface for ollama.ai, a tool that enables running Large Language Models (LLMs) on your local machine. It provides a user-friendly platform for chatting with LLMs and accessing various models for text generation. Users can easily interact with different models, manage chat history, and explore available models through the web interface. The tool is built with Vue.js, Vite, and Tailwind CSS, offering a modern and responsive design for seamless user experience.

The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.

rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

Honcho is a platform for creating personalized AI agents and LLM powered applications for end users. The repository is a monorepo containing the server/API for managing database interactions and storing application state, along with a Python SDK. It utilizes FastAPI for user context management and Poetry for dependency management. The API can be run using Docker or manually by setting environment variables. The client SDK can be installed using pip or Poetry. The project is open source and welcomes contributions, following a fork and PR workflow. Honcho is licensed under the AGPL-3.0 License.

OpenSumi is a framework designed to help users quickly build AI Native IDE products. It provides a set of tools and templates for creating Cloud IDEs, Desktop IDEs based on Electron, CodeBlitz web IDE Framework, Lite Web IDE on the Browser, and Mini-App liked IDE. The framework also offers documentation for users to refer to and a detailed guide on contributing to the project. OpenSumi encourages contributions from the community and provides a platform for users to report bugs, contribute code, or improve documentation. The project is licensed under the MIT license and contains third-party code under other open source licenses.

The Ultralytics YOLO iOS App GitHub repository offers an advanced object detection tool leveraging YOLOv8 models for iOS devices. Users can transform their devices into intelligent detection tools to explore the world in a new and exciting way. The app provides real-time detection capabilities with multiple AI models to choose from, ranging from 'nano' to 'x-large'. Contributors are welcome to participate in this open-source project, and licensing options include AGPL-3.0 for open-source use and an Enterprise License for commercial integration. Users can easily set up the app by following the provided steps, including cloning the repository, adding YOLOv8 models, and running the app on their iOS devices.

PyAirbyte brings the power of Airbyte to every Python developer by providing a set of utilities to use Airbyte connectors in Python. It enables users to easily manage secrets, work with various connectors like GitHub, Shopify, and Postgres, and contribute to the project. PyAirbyte is not a replacement for Airbyte but complements it, supporting data orchestration frameworks like Airflow and Snowpark. Users can develop ETL pipelines and import connectors from local directories. The tool simplifies data integration tasks for Python developers.

MD-Agent is a LLM-agent based toolset for Molecular Dynamics. It uses Langchain and a collection of tools to set up and execute molecular dynamics simulations, particularly in OpenMM. The tool assists in environment setup, installation, and usage by providing detailed steps. It also requires API keys for certain functionalities, such as OpenAI and paper-qa for literature searches. Contributions to the project are welcome, with a detailed Contributor's Guide available for interested individuals.

FlowGen is a tool built for AutoGen, a great agent framework from Microsoft and a lot of contributors. It provides intuitive visual tools that streamline the construction and oversight of complex agent-based workflows, simplifying the process for creators and developers. Users can create Autoflows, chat with agents, and share flow templates. The tool is fully dockerized and supports deployment on Railway.app. Contributions to the project are welcome, and the platform uses semantic-release for versioning and releases.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

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.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.