aicommit is a small command line tool for generating commit messages that follow the repository's existing style. It helps users create commit messages with intention, context, and external references to aid understanding of code changes. The tool offers flags like `-c` for adding context and supports retrying and dry-running commit messages. Users can also provide context to the AI for better message generation and save API key to disk for convenience. aicommit reads a `COMMITS.md` file to determine the style guide, following it if available.

VectorFlow is an open source, high throughput, fault tolerant vector embedding pipeline. It provides a simple API endpoint for ingesting large volumes of raw data, processing, and storing or returning the vectors quickly and reliably. The tool supports text-based files like TXT, PDF, HTML, and DOCX, and can be run locally with Kubernetes in production. VectorFlow offers functionalities like embedding documents, running chunking schemas, custom chunking, and integrating with vector databases like Pinecone, Qdrant, and Weaviate. It enforces a standardized schema for uploading data to a vector store and supports features like raw embeddings webhook, chunk validation webhook, S3 endpoint, and telemetry. The tool can be used with the Python client and provides detailed instructions for running and testing the functionalities.

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.

Whisper Dictation is a fast, offline, privacy-focused tool for voice typing, AI voice chat, voice control, and translation. It allows hands-free operation, launching and controlling apps, and communicating with OpenAI ChatGPT or a local chat server. The tool also offers the option to speak answers out loud and draw pictures. It includes client and server versions, inspired by the Star Trek series, and is designed to keep data off the internet and confidential. The project is optimized for dictation and translation tasks, with voice control capabilities and AI image generation using stable-diffusion API.

AlwaysReddy is a simple LLM assistant with no UI that you interact with entirely using hotkeys. It can easily read from or write to your clipboard, and voice chat with you via TTS and STT. Here are some of the things you can use AlwaysReddy for: - Explain a new concept to AlwaysReddy and have it save the concept (in roughly your words) into a note. - Ask AlwaysReddy "What is X called?" when you know how to roughly describe something but can't remember what it is called. - Have AlwaysReddy proofread the text in your clipboard before you send it. - Ask AlwaysReddy "From the comments in my clipboard, what do the r/LocalLLaMA users think of X?" - Quickly list what you have done today and get AlwaysReddy to write a journal entry to your clipboard before you shutdown the computer for the day.

QANTA is a system and dataset for question answering tasks. It provides a script to download datasets, preprocesses questions, and matches them with Wikipedia pages. The system includes various datasets, training, dev, and test data in JSON and SQLite formats. Dependencies include Python 3.6, `click`, and NLTK models. Elastic Search 5.6 is needed for the Guesser component. Configuration is managed through environment variables and YAML files. QANTA supports multiple guesser implementations that can be enabled/disabled. Running QANTA involves using `cli.py` and Luigi pipelines. The system accesses raw Wikipedia dumps for data processing. The QANTA ID numbering scheme categorizes datasets based on events and competitions.

gemini-cli is a versatile command-line interface for Google's Gemini LLMs, written in Go. It includes tools for chatting with models, generating/comparing embeddings, and storing data in SQLite for analysis. Users can interact with Gemini models through various subcommands like prompt, chat, counttok, embed content, embed db, and embed similar.

Oterm is a text-based terminal client for Ollama, a large language model. It provides an intuitive and simple terminal UI, allowing users to interact with Ollama without running servers or frontends. Oterm supports multiple persistent chat sessions, which are stored along with context embeddings and system prompt customizations in a SQLite database. Users can easily customize the model's system prompt and parameters, and select from any of the models they have pulled in Ollama or their own custom models. Oterm also supports keyboard shortcuts for creating new chat sessions, editing existing sessions, renaming sessions, exporting sessions as markdown, deleting sessions, toggling between dark and light themes, quitting the application, switching to multiline input mode, selecting images to include with messages, and navigating through the history of previous prompts. Oterm is licensed under the MIT License.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

Tiny-Predictive-Text is a demonstration of predictive text without an LLM, using permy.link. It provides a detailed description of the tool, including its features, benefits, and how to use it. The tool is suitable for a variety of jobs, including content writers, editors, and researchers. It can be used to perform a variety of tasks, such as generating text, completing sentences, and correcting errors.

Lightning Lab is a public template for artificial intelligence and machine learning research projects using Lightning AI's PyTorch Lightning. It provides a structured project layout with modules for command line interface, experiment utilities, Lightning Module and Trainer, data acquisition and preprocessing, model serving APIs, project configurations, training checkpoints, technical documentation, logs, notebooks for data analysis, requirements management, testing, and packaging. The template simplifies the setup of deep learning projects and offers extras for different domains like vision, text, audio, reinforcement learning, and forecasting.

BlinkID iOS is a mobile SDK that enables developers to easily integrate ID scanning and data extraction capabilities into their iOS applications. The SDK supports scanning and processing various types of identity documents, such as passports, driver's licenses, and ID cards. It provides accurate and fast data extraction, including personal information and document details. With BlinkID iOS, developers can enhance their apps with secure and reliable ID verification functionality, improving user experience and streamlining identity verification processes.

ChatGPT Smart Speaker is a project that enables speech recognition and text-to-speech functionalities using OpenAI and Google Speech Recognition. It provides scripts for running on PC/Mac and Raspberry Pi, allowing users to interact with a smart speaker setup. The project includes detailed instructions for setting up the required hardware and software dependencies, along with customization options for the OpenAI model engine, language settings, and response randomness control. The Raspberry Pi setup involves utilizing the ReSpeaker hardware for voice feedback and light shows. The project aims to offer an advanced smart speaker experience with features like wake word detection and response generation using AI models.

This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

aicommit is a small command line tool for generating commit messages that follow the repository's existing style. It helps users create commit messages with intention, context, and external references to aid understanding of code changes. The tool offers flags like `-c` for adding context and supports retrying and dry-running commit messages. Users can also provide context to the AI for better message generation and save API key to disk for convenience. aicommit reads a `COMMITS.md` file to determine the style guide, following it if available.

Twinny is a free and open-source AI code completion plugin for Visual Studio Code and compatible editors. It integrates with various tools and frameworks, including Ollama, llama.cpp, oobabooga/text-generation-webui, LM Studio, LiteLLM, and Open WebUI. Twinny offers features such as fill-in-the-middle code completion, chat with AI about your code, customizable API endpoints, and support for single or multiline fill-in-middle completions. It is easy to install via the Visual Studio Code extensions marketplace and provides a range of customization options. Twinny supports both online and offline operation and conforms to the OpenAI API standard.

CodeGPT is an extension for JetBrains IDEs that provides access to state-of-the-art large language models (LLMs) for coding assistance. It offers a range of features to enhance the coding experience, including code completions, a ChatGPT-like interface for instant coding advice, commit message generation, reference file support, name suggestions, and offline development support. CodeGPT is designed to keep privacy in mind, ensuring that user data remains secure and private.

This AI-powered git commit plugin for VSCode streamlines your commit and push processes, eliminating the need for manual confirmation. With a focus on minimizing keystrokes, the plugin leverages LLM to generate commit messages and automate the entire process. Key features include AI-assisted git commit and push, eliminating the need for the 'git add .' command, and customizable OpenAI model selection. The plugin supports multiple languages, making it accessible to developers worldwide. Additionally, it offers advanced settings for specifying the OpenAI API key, base URL, and conventional commit format. Developers can contribute to the project by following the provided development instructions.

AI Commits is a plugin for IntelliJ-based IDEs and Android Studio that generates commit messages using git diff and OpenAI. It offers features such as generating commit messages from diff using OpenAI API, computing diff only from selected files and lines in the commit dialog, creating custom prompts for commit message generation, using predefined variables and hints to customize prompts, choosing any of the models available in OpenAI API, setting OpenAI network proxy, and setting custom OpenAI compatible API endpoint.

AICommit2 is a Reactive CLI tool that streamlines interactions with various AI providers such as OpenAI, Anthropic Claude, Gemini, Mistral AI, Cohere, and unofficial providers like Huggingface and Clova X. Users can request multiple AI simultaneously to generate git commit messages without waiting for all AI responses. The tool runs 'git diff' to grab code changes, sends them to configured AI, and returns the AI-generated commit message. Users can set API keys or Cookies for different providers and configure options like locale, generate number of messages, commit type, proxy, timeout, max-length, and more. AICommit2 can be used both locally with Ollama and remotely with supported providers, offering flexibility and efficiency in generating commit messages.

Lobe CLI Toolbox is an AI CLI Toolbox designed to enhance git commit and i18n workflow efficiency. It includes tools like Lobe Commit for generating Gitmoji-based commit messages and Lobe i18n for automating the i18n translation process. The toolbox also features Lobe label for automatically copying issues labels from a template repo. It supports features such as automatic splitting of large files, incremental updates, and customization options for the OpenAI model, API proxy, and temperature.

OpenCommit is a tool that auto-generates meaningful commits using AI, allowing users to quickly create commit messages for their staged changes. It provides a CLI interface for easy usage and supports customization of commit descriptions, emojis, and AI models. Users can configure local and global settings, switch between different AI providers, and set up Git hooks for integration with IDE Source Control. Additionally, OpenCommit can be used as a GitHub Action to automatically improve commit messages on push events, ensuring all commits are meaningful and not generic. Payments for OpenAI API requests are handled by the user, with the tool storing API keys locally.

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.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

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

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.