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Token Counter is an AI tool designed to convert text input into tokens for various AI models. It helps users accurately determine the token count and associated costs when working with AI models. By providing insights into tokenization strategies and cost structures, Token Counter streamlines the process of utilizing advanced technologies.

CallCare is an AI-driven application designed to provide peace of mind for users concerned about the mental wellness of their elderly loved ones. The platform allows users to schedule intelligent calls to monitor the well-being of their loved ones, analyze call details with advanced analytics, and track mental wellness metrics through detailed reports. With a focus on mimicking human interaction, CallCare offers a secure and private environment for engaging conversations that aim to detect any mental wellness decline early on.

CrowdPrisma is an AI-powered survey analysis tool that revolutionizes the way insights are extracted from free-text responses. It utilizes a cutting-edge TextEngine to automatically group responses into coherent themes and topics, providing users with quantitative insights in minutes. The platform offers a highly interactive dashboard for in-depth analysis, multilingual support, fair billing practices, and advanced features for market research, customer experience analysis, human resources, and policy research.

HotCheck is a web application that allows users to discover their hotness rating by uploading a photo of themselves. In addition to providing a hotness rating, the app also offers other fun information about the uploaded picture. Users can easily share their results on social media platforms like WhatsApp and Twitter. HotCheck aims to be a fun and entertaining tool for users to gauge their attractiveness based on the uploaded photo.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs). By simulating real-world inference workloads, GuideLLM helps users gauge the performance, resource needs, and cost implications of deploying LLMs on various hardware configurations. This approach ensures efficient, scalable, and cost-effective LLM inference serving while maintaining high service quality. Key features include performance evaluation, resource optimization, cost estimation, and scalability testing.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

This repository evaluates Large Language Models (LLMs) trained on PGN format chess games using linear probes. It assesses the LLMs' internal understanding of board state and their ability to estimate player skill levels. The repo provides tools to train, evaluate, and visualize linear probes on LLMs trained to play chess with PGN strings. Users can visualize the model's predictions, perform interventions on the model's internal board state, and analyze board state and player skill level accuracy across different LLMs. The experiments in the repo can be conducted with less than 1 GB of VRAM, and training probes on the 8 layer model takes about 10 minutes on an RTX 3050. The repo also includes scripts for performing board state interventions and skill interventions, along with useful links to open-source code, models, datasets, and pretrained models.

MarkLLM is an open-source toolkit designed for watermarking technologies within large language models (LLMs). It simplifies access, understanding, and assessment of watermarking technologies, supporting various algorithms, visualization tools, and evaluation modules. The toolkit aids researchers and the community in ensuring the authenticity and origin of machine-generated text.

SuperKnowa is a fast framework to build Enterprise RAG (Retriever Augmented Generation) Pipelines at Scale, powered by watsonx. It accelerates Enterprise Generative AI applications to get prod-ready solutions quickly on private data. The framework provides pluggable components for tackling various Generative AI use cases using Large Language Models (LLMs), allowing users to assemble building blocks to address challenges in AI-driven text generation. SuperKnowa is battle-tested from 1M to 200M private knowledge base & scaled to billions of retriever tokens.

The vscode-pddl extension provides comprehensive support for Planning Domain Description Language (PDDL) in Visual Studio Code. It enables users to model planning domains, validate them, industrialize planning solutions, and run planners. The extension offers features like syntax highlighting, auto-completion, plan visualization, plan validation, plan happenings evaluation, search debugging, and integration with Planning.Domains. Users can create PDDL files, run planners, visualize plans, and debug search algorithms efficiently within VS Code.

Home-Gallery.org is a self-hosted open-source web gallery for browsing personal photos and videos with tagging, mobile-friendly interface, and AI-powered image and face discovery. It aims to provide a fast user experience on mobile phones and help users browse and rediscover memories from their media archive. The tool allows users to serve their local data without relying on cloud services, view photos and videos from mobile phones, and manage images from multiple media source directories. Features include endless photo stream, video transcoding, reverse image lookup, face detection, GEO location reverse lookups, tagging, and more. The tool runs on NodeJS and supports various platforms like Linux, Mac, and Windows.

Algolisted is a pioneering platform dedicated to algorithmic problem-solving, offering a centralized hub for a diverse array of algorithmic challenges. It provides an immersive online environment for programmers to enhance their skills through Data Structures and Algorithms (DSA) sheets, academic progress tracking, resume refinement with OpenAI integration, adaptive testing, and job opportunity listings. The project is built on the MERN stack, Flask, Beautiful Soup, and Selenium,GEN AI, and deployed on Firebase. Algolisted aims to be a reliable companion in the pursuit of coding knowledge and proficiency.

Kairon is an open-source conversational digital transformation platform that helps build LLM-based digital assistants at scale. It provides a no-coding web interface for adapting, training, testing, and maintaining AI assistants. Kairon focuses on pre-processing data for chatbots, including question augmentation, knowledge graph generation, and post-processing metrics. It offers end-to-end lifecycle management, low-code/no-code interface, secure script injection, telemetry monitoring, chat client designer, analytics module, and real-time struggle analytics. Kairon is suitable for teams and individuals looking for an easy interface to create, train, test, and deploy digital assistants.

Middleware is an open-source engineering management tool that helps engineering leaders measure and analyze team effectiveness using DORA metrics. It integrates with CI/CD tools, automates DORA metric collection and analysis, visualizes key performance indicators, provides customizable reports and dashboards, and integrates with project management platforms. Users can set up Middleware using Docker or manually, generate encryption keys, set up backend and web servers, and access the application to view DORA metrics. The tool calculates DORA metrics using GitHub data, including Deployment Frequency, Lead Time for Changes, Mean Time to Restore, and Change Failure Rate. Middleware aims to provide DORA metrics to users based on their Git data, simplifying the process of tracking software delivery performance and operational efficiency.

The 'llm-misinformation-survey' repository is dedicated to the survey on combating misinformation in the age of Large Language Models (LLMs). It explores the opportunities and challenges of utilizing LLMs to combat misinformation, providing insights into the history of combating misinformation, current efforts, and future outlook. The repository serves as a resource hub for the initiative 'LLMs Meet Misinformation' and welcomes contributions of relevant research papers and resources. The goal is to facilitate interdisciplinary efforts in combating LLM-generated misinformation and promoting the responsible use of LLMs in fighting misinformation.

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