Best AI tools for< Tie A Knot >

Cardinal is an AI-powered product backlog tool that helps product managers prioritize features and make data-driven decisions. It integrates with your CRM and customer support tools to collect customer feedback and revenue data, which it then uses to identify the most valuable features to build. Cardinal also provides a clear view of your product roadmap and progress, so you can always see what's coming up and how it's aligned with your business goals.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

AI-Toolbox is a C++ library aimed at representing and solving common AI problems, with a focus on MDPs, POMDPs, and related algorithms. It provides an easy-to-use interface that is extensible to many problems while maintaining readable code. The toolbox includes tutorials for beginners in reinforcement learning and offers Python bindings for seamless integration. It features utilities for combinatorics, polytopes, linear programming, sampling, distributions, statistics, belief updating, data structures, logging, seeding, and more. Additionally, it supports bandit/normal games, single agent MDP/stochastic games, single agent POMDP, and factored/joint multi-agent scenarios.

WildBench is a tool designed for benchmarking Large Language Models (LLMs) with challenging tasks sourced from real users in the wild. It provides a platform for evaluating the performance of various models on a range of tasks. Users can easily add new models to the benchmark by following the provided guidelines. The tool supports models from Hugging Face and other APIs, allowing for comprehensive evaluation and comparison. WildBench facilitates running inference and evaluation scripts, enabling users to contribute to the benchmark and collaborate on improving model performance.

EduChat is a large-scale language model-based chatbot system designed for intelligent education by the EduNLP team at East China Normal University. The project focuses on developing a dialogue-based language model for the education vertical domain, integrating diverse education vertical domain data, and providing functions such as automatic question generation, homework correction, emotional support, course guidance, and college entrance examination consultation. The tool aims to serve teachers, students, and parents to achieve personalized, fair, and warm intelligent education.

Llama3.java is a practical Llama 3 inference tool implemented in a single Java file. It serves as the successor of llama2.java and is designed for testing and tuning compiler optimizations and features on the JVM, especially for the Graal compiler. The tool features a GGUF format parser, Llama 3 tokenizer, Grouped-Query Attention inference, support for Q8_0 and Q4_0 quantizations, fast matrix-vector multiplication routines using Java's Vector API, and a simple CLI with 'chat' and 'instruct' modes. Users can download quantized .gguf files from huggingface.co for model usage and can also manually quantize to pure 'Q4_0'. The tool requires Java 21+ and supports running from source or building a JAR file for execution. Performance benchmarks show varying tokens/s rates for different models and implementations on different hardware setups.

K8sGateway is a feature-rich, fast, and flexible Kubernetes-native API gateway built on Envoy proxy and Kubernetes Gateway API. It excels in function-level routing, supports legacy apps, microservices, and serverless. It offers robust discovery capabilities, seamless integration with open-source projects, and supports hybrid applications with various technologies, architectures, protocols, and clouds.

LitGPT is a command-line tool designed to easily finetune, pretrain, evaluate, and deploy 20+ LLMs **on your own data**. It features highly-optimized training recipes for the world's most powerful open-source large-language-models (LLMs).

Burr is a Python library and UI that makes it easy to develop applications that make decisions based on state (chatbots, agents, simulations, etc...). Burr includes a UI that can track/monitor those decisions in real time.

llm-export is a tool for exporting llm models to onnx and mnn formats. It has features such as passing onnxruntime correctness tests, optimizing the original code to support dynamic shapes, reducing constant parts, optimizing onnx models using OnnxSlim for performance improvement, and exporting lora weights to onnx and mnn formats. Users can clone the project locally, clone the desired LLM project locally, and use LLMExporter to export the model. The tool supports various export options like exporting the entire model as one onnx model, exporting model segments as multiple models, exporting model vocabulary to a text file, exporting specific model layers like Embedding and lm_head, testing the model with queries, validating onnx model consistency with onnxruntime, converting onnx models to mnn models, and more. Users can specify export paths, skip optimization steps, and merge lora weights before exporting.

AzureOpenAI-with-APIM is a repository that provides a one-button deploy solution for Azure API Management (APIM), Key Vault, and Log Analytics to work seamlessly with Azure OpenAI endpoints. It enables organizations to scale and manage their Azure OpenAI service efficiently by issuing subscription keys via APIM, delivering usage metrics, and implementing policies for access control and cost management. The repository offers detailed guidance on implementing APIM to enhance Azure OpenAI resiliency, scalability, performance, monitoring, and chargeback capabilities.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

AI Agents Masterclass is a repository dedicated to teaching developers how to use AI agents to transform businesses and create powerful software. It provides weekly videos with accompanying code folders, guiding users on setting up Python environments, using environment variables, and installing necessary packages to run the code. The focus is on Large Language Models that can interact with the outside world to perform tasks like drafting emails, booking appointments, and managing tasks, enabling users to create innovative applications with minimal coding effort.

Anki AI Utils is a powerful suite of AI-powered tools designed to enhance your Anki flashcard learning experience by automatically improving cards you struggle with. The tools include features such as adaptive learning, personalized memory hooks, automation readiness, universal compatibility, provider agnosticism, and infinite extensibility. The toolkit consists of tools like Illustrator for creating custom mnemonic images, Reformulator for rephrasing flashcards, Mnemonics Creator for generating memorable mnemonics, Explainer for providing detailed explanations, and Mnemonics Helper for quick mnemonic generation. The project aims to motivate others to package the tools into addons for wider accessibility.

Wunjo AI is a comprehensive tool that empowers users to explore the realm of speech synthesis, deepfake animations, video-to-video transformations, and more. Its user-friendly interface and privacy-first approach make it accessible to both beginners and professionals alike. With Wunjo AI, you can effortlessly convert text into human-like speech, clone voices from audio files, create multi-dialogues with distinct voice profiles, and perform real-time speech recognition. Additionally, you can animate faces using just one photo combined with audio, swap faces in videos, GIFs, and photos, and even remove unwanted objects or enhance the quality of your deepfakes using the AI Retouch Tool. Wunjo AI is an all-in-one solution for your voice and visual AI needs, offering endless possibilities for creativity and expression.

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

This repository contains a curated list of cutting-edge works in Simultaneous Localization and Mapping (SLAM). It includes research papers, projects, and tools related to various aspects of SLAM, such as 3D reconstruction, semantic mapping, novel algorithms, large-scale mapping, and more. The repository aims to showcase the latest advancements in SLAM technology and provide resources for researchers and practitioners in the field.

CritiqueLLM is an official implementation of a model designed for generating informative critiques to evaluate large language model generation. It includes functionalities for data collection, referenced pointwise grading, referenced pairwise comparison, reference-free pairwise comparison, reference-free pointwise grading, inference for pointwise grading and pairwise comparison, and evaluation of the generated results. The model aims to provide a comprehensive framework for evaluating the performance of large language models based on human ratings and comparisons.

The 'ollama-ebook-summary' repository is a Python project that creates bulleted notes summaries of books and long texts, particularly in epub and pdf formats with ToC metadata. It automates the extraction of chapters, splits them into ~2000 token chunks, and allows for asking arbitrary questions to parts of the text for improved granularity of response. The tool aims to provide summaries for each page of a book rather than a one-page summary of the entire document, enhancing content curation and knowledge sharing capabilities.

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