llmstxt-site is a centralized directory for /llms.txt files, a proposed standard for websites to provide concise and structured information for large language models (LLMs) during inference time. The project aims to curate a comprehensive list of /llms.txt files, provide a platform for sharing and updating resources, and support the adoption of /llms.txt as a standard for LLM-friendly content.

Cohere's Build-Your-Own-Connector framework allows integration of Cohere's Command LLM via the Chat API endpoint to any datastore/software holding text information with a search endpoint. Enables user queries grounded in proprietary information. Use-cases include question/answering, knowledge working, comms summary, and research. Repository provides code for popular datastores and a template connector. Requires Python 3.11+ and Poetry. Connectors can be built and deployed using Docker. Environment variables set authorization values. Pre-commits for linting. Connectors tailored to integrate with Cohere's Chat API for creating chatbots. Connectors return documents as JSON objects for Cohere's API to generate answers with citations.

PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.

RAFT (Retrieval-Augmented Fine-Tuning) is a method for creating conversational agents that realistically emulate specific human targets. It involves a dual-phase process of fine-tuning and retrieval-based augmentation to generate nuanced and personalized dialogue. The tool is designed to combine interview transcripts with memories from past writings to enhance language model responses. RAFT has the potential to advance the field of personalized, context-sensitive conversational agents.

This repository is part of Intelligent Document Processing with AWS AI Services workshop. It aims to automate the extraction of information from complex content in various document formats such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts using AWS Machine Learning services like Amazon Textract and Amazon Comprehend. The repository provides hands-on labs to familiarize users with these AI services and build solutions to automate business processes that rely on manual inputs and intervention across different file types and formats.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

This repository provides a sample solution for building generative AI agents using Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain. The solution creates a generative AI financial services agent capable of assisting users with account information, loan applications, and answering natural language questions. It serves as a launchpad for developers to create personalized conversational agents for applications like chatbots and virtual assistants.

Chinese-Tiny-LLM is a repository containing procedures for cleaning Chinese web corpora and pre-training code. It introduces CT-LLM, a 2B parameter language model focused on the Chinese language. The model primarily uses Chinese data from a 1,200 billion token corpus, showing excellent performance in Chinese language tasks. The repository includes tools for filtering, deduplication, and pre-training, aiming to encourage further research and innovation in language model development.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

Flux is a powerful tool for interacting with large language models (LLMs) that generates multiple completions per prompt in a tree structure and lets you explore the best ones in parallel. Flux's tree structure allows you to get a wider variety of creative responses, test out different prompts with the same shared context, and use inconsistencies to identify where the model is uncertain. It also provides a robust set of keyboard shortcuts, allows setting the system message and editing GPT messages, autosaves to local storage, uses the OpenAI API directly, and is 100% open source and MIT licensed.

AIS-catcher: A multi-platform AIS Receiver

AliceVision is a photogrammetric computer vision framework which provides a 3D reconstruction pipeline. It is designed to process images from different viewpoints and create detailed 3D models of objects or scenes. The framework includes various algorithms for feature detection, matching, and structure from motion. AliceVision is suitable for researchers, developers, and enthusiasts interested in computer vision, photogrammetry, and 3D modeling. It can be used for applications such as creating 3D models of buildings, archaeological sites, or objects for virtual reality and augmented reality experiences.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

AirAPI_Windows is a work in progress driver for the Nreal Air glasses, providing native windows support without nebula. The project aims to address slight drift in fusion configuration and is a collaborative effort by the community to offer open access to the glasses. Users can access precompiled AirAPI_Windows.dll along with hidapi.dll for usage. The tool allows building from source, including getting hidapi, cloning the project, and building Fusion and AirAPI_Windows DLL. The project is supported by contributors like abls, edwatt, and jakedowns, and users can show appreciation by buying a coffee for msmithdev.

IKBT is a Python-based system for generating closed-form solutions to the manipulator inverse kinematics problem using behavior trees for action selection. Solutions are fully symbolic and are output as LaTex, Python, and C++. The tool automates closed-form kinematics solving by organizing solution algorithms in a behavior tree, incorporating frequently used knowledge, generating a dependency graph of joint variables, and providing features for automatic documentation and code generation. It is implemented in Python with minimal dependencies outside of the standard Python distribution.

Parsee AI is a high-level open source data extraction and structuring framework specialized for the extraction of data from a financial domain, but can be used for other use-cases as well. It aims to make the structuring of data from unstructured sources like PDFs, HTML files, and images as easy as possible. Parsee can be used locally in Python environments or through a hosted version for cloud-based jobs. It supports the extraction of tables, numbers, and other data elements, with the ability to create custom extraction templates and run jobs using different models.

llmstxt-site is a centralized directory for /llms.txt files, a proposed standard for websites to provide concise and structured information for large language models (LLMs) during inference time. The project aims to curate a comprehensive list of /llms.txt files, provide a platform for sharing and updating resources, and support the adoption of /llms.txt as a standard for LLM-friendly content.

Venice is a derived data storage platform, providing the following characteristics: 1. High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza). 2. Low latency online reads via remote queries or in-process caching. 3. Active-active replication between regions with CRDT-based conflict resolution. 4. Multi-cluster support within each region with operator-driven cluster assignment. 5. Multi-tenancy, horizontal scalability and elasticity within each cluster. The above makes Venice particularly suitable as the stateful component backing a Feature Store, such as Feathr. AI applications feed the output of their ML training jobs into Venice and then query the data for use during online inference workloads.

The URL/API Source is a plugin for OBS Studio that allows users to add a media source fetching data from a URL or API endpoint and displaying it as text. It supports input and output templating, various request types, output parsing (JSON, XML/HTML, Regex, CSS selectors), live data updating, output styling, and formatting. Future features include authentication, websocket support, more parsing options, request types, and output formats. The plugin is cross-platform compatible and actively maintained by the developer. Users can support the project on GitHub.

A simple Golang package to access the Airtable API. It provides functionalities to interact with Airtable such as initializing client, getting tables, listing records, adding records, updating records, deleting records, and bulk deleting records. The package is compatible with Go 1.13 and above.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

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.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.