Best AI tools for< Model Data >

Softbuilder is a software development company that focuses on creating innovative database tools. Their products include AbstraLinx, a powerful metadata discovery tool for Salesforce; ERBuilder Data Modeler, an easy-to-use database modeling software; and SB Data Generator, a tool for generating and populating databases with realistic test data. Softbuilder's tools aim to unlock limitless potential for businesses, teams, and databases by providing straightforward solutions that leverage the latest technology to enhance productivity and focus on delivering solutions.

xAI Grok is a visual analytics platform that helps users understand and interpret machine learning models. It provides a variety of tools for visualizing and exploring model data, including interactive charts, graphs, and tables. xAI Grok also includes a library of pre-built visualizations that can be used to quickly get started with model analysis.

Zesh AI is an advanced AI-powered ecosystem that offers a range of innovative tools and solutions for Web3 projects, community managers, data analysts, and decision-makers. It leverages AI Agents and LLMs to redefine KOL analysis, community engagement, and campaign optimization. With features like InfluenceAI for KOL discovery, EngageAI for campaign management, IDAI for fraud detection, AnalyticsAI for data analysis, and Wallet & NFT Profile for community empowerment, Zesh AI provides cutting-edge solutions for various aspects of Web3 ecosystems.

Rerun is an SDK, time-series database, and visualizer for temporal and multimodal data. It is used in fields like robotics, spatial computing, 2D/3D simulation, and finance to verify, debug, and explain data. Rerun allows users to log data like tensors, point clouds, and text to create streams, visualize and interact with live and recorded streams, build layouts, customize visualizations, and extend data and UI functionalities. The application provides a composable data model, dynamic schemas, and custom views for enhanced data visualization and analysis.

Vizologi is an AI-powered innovation management software that helps businesses generate limitless business ideas, gain insights on markets and competitors, and automate business plan creation. It offers a stunning business model canvas database with thousands of examples, and allows users to create and edit unique business plans using the mash-up method. Vizologi has helped thousands of people complete over 1.5 million questions in 25+ thousand projects.

Bethge Lab is an AI research group at the University of Tübingen focusing on Neuro AI - Autonomous Lifelong Learning in Machines and Brains. They develop machine learning tools for neural data analysis and draw inspiration from the brain to address key problems in machine learning. Their research includes representation learning, probabilistic inference, generative modeling, behavioral data analysis, and neural data analysis. Additionally, they explore AI sciencepreneurship and collaborate with startups. Bethge Lab aims to advance the understanding of autonomous learning and develop economically feasible solutions for long-term human needs.

Tactic is an AI-powered platform that provides generative insights and solutions for customers by leveraging AI technology to generate target accounts unique to businesses and new customer insights from various data sources. It offers features such as no-code custom AI builder, process automation, multi-step reasoning, model agnostic data import, and simple user experience. Tactic is trusted by hypergrowth startups and Fortune 500 companies for market research, audience automation, and customer data management. The platform helps users increase revenue, save time on research and analysis, and close more deals efficiently.

Kore.ai is an enterprise AI platform offering AI solutions for workforce, business, and customer needs. It provides powerful workspace search, pre-built AI agents for HR, Recruiting, and IT, as well as customizable no-code tools to build custom AI agents. The platform also offers AI for process automation with no-code tools, pre-built prompts, and agent templates, along with AI model and data management capabilities. Additionally, Kore.ai provides AI for service to enhance customer experience with no-code tools to build conversational AI agents and AI support for service reps.

Datagen is a platform that provides synthetic data for computer vision. Synthetic data is artificially generated data that can be used to train machine learning models. Datagen's data is generated using a variety of techniques, including 3D modeling, computer graphics, and machine learning. The company's data is used by a variety of industries, including automotive, security, smart office, fitness, cosmetics, and facial applications.

Datacog is an AI application that offers a comprehensive solution for efficient data warehouse management, application integration, and machine learning. It enables organizations to leverage the complete capabilities of their data assets through intuitive data organization and model training features. With zero configuration, instant deployment, scalability, and real-time monitoring, Datacog simplifies model training and streamlines decision-making. Join the ranks of industry leaders who have harnessed the power of organized data and automation with Datacog.

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Striveworks is an AI application that offers a Machine Learning Operations Platform designed to help organizations build, deploy, maintain, monitor, and audit machine learning models efficiently. It provides features such as rapid model deployment, data and model auditability, low-code interface, flexible deployment options, and operationalizing AI data science with real returns. Striveworks aims to accelerate the ML lifecycle, save time and money in model creation, and enable non-experts to leverage AI for data-driven decisions.

OpenLIT is an AI application designed as an Observability tool for GenAI and LLM applications. It empowers model understanding and data visualization through an interactive Learning Interpretability Tool. With OpenTelemetry-native support, it seamlessly integrates into projects, offering features like fine-tuning performance, real-time data streaming, low latency processing, and visualizing data insights. The tool simplifies monitoring with easy installation and light/dark mode options, connecting to popular observability platforms for data export. Committed to OpenTelemetry community standards, OpenLIT provides valuable insights to enhance application performance and reliability.

Breadcrumbs is a revenue acceleration platform that helps businesses optimize their entire sales and marketing funnel. It provides enterprise-grade lead scoring, allowing businesses to identify and prioritize their most promising leads. Breadcrumbs also offers a range of other features, such as data-driven model creation, unlimited workspaces and models, multi-variate testing, and integrations with a variety of marketing and sales tools. With Breadcrumbs, businesses can improve their lead quality, increase conversion rates, and accelerate revenue growth.

Whybug is an AI tool designed to help developers troubleshoot and fix coding errors efficiently. By leveraging a large language model trained on data from StackExchange and other sources, Whybug can analyze error messages, identify root causes, and provide suggestions for resolution. Users can simply paste an error message into the tool and receive detailed explanations and example fixes. With Whybug, developers can streamline the debugging process and improve code quality.

Domino Data Lab is an enterprise AI platform that enables data scientists and IT leaders to build, deploy, and manage AI models at scale. It provides a unified platform for accessing data, tools, compute, models, and projects across any environment. Domino also fosters collaboration, establishes best practices, and tracks models in production to accelerate and scale AI while ensuring governance and reducing costs.

Datasaur is an advanced text and audio data labeling platform that offers customizable solutions for various industries such as LegalTech, Healthcare, Financial, Media, e-Commerce, and Government. It provides features like configurable annotation, quality control automation, and workforce management to enhance the efficiency of NLP and LLM projects. Datasaur prioritizes data security with military-grade practices and offers seamless integrations with AWS and other technologies. The platform aims to streamline the data labeling process, allowing engineers to focus on creating high-quality models.

Denvr DataWorks AI Cloud is a cloud-based AI platform that provides end-to-end AI solutions for businesses. It offers a range of features including high-performance GPUs, scalable infrastructure, ultra-efficient workflows, and cost efficiency. Denvr DataWorks is an NVIDIA Elite Partner for Compute, and its platform is used by leading AI companies to develop and deploy innovative AI solutions.

Enhans AI Model Generator is an advanced AI tool designed to help users generate AI models efficiently. It utilizes cutting-edge algorithms and machine learning techniques to streamline the model creation process. With Enhans AI Model Generator, users can easily input their data, select the desired parameters, and obtain a customized AI model tailored to their specific needs. The tool is user-friendly and does not require extensive programming knowledge, making it accessible to a wide range of users, from beginners to experts in the field of AI.

Flux LoRA Model Library is an AI tool that provides a platform for finding and using Flux LoRA models suitable for various projects. Users can browse a catalog of popular Flux LoRA models and learn about FLUX models and LoRA (Low-Rank Adaptation) technology. The platform offers resources for fine-tuning models and ensuring responsible use of generated images.

OpenModelDB is a community-driven database of AI upscaling models, providing a centralized platform for users to access and compare various models. The repository contains a collection of models and model metadata, facilitating easy exploration and evaluation of different AI upscaling solutions. With a focus on enhancing the accessibility and usability of AI models, OpenModelDB aims to streamline the process of finding and selecting the most suitable models for specific tasks or projects.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

CSGHub Server is a part of the open source and reliable large model assets management platform - CSGHub. It focuses on management of models, datasets, and other LLM assets through REST API. Key features include creation and management of users and organizations, auto-tagging of model and dataset labels, search functionality, online preview of dataset files, content moderation for text and image, download of individual files, tracking of model and dataset activity data. The tool is extensible and customizable, supporting different git servers, flexible LFS storage system configuration, and content moderation options. The roadmap includes support for more Git servers, Git LFS, dataset online viewer, model/dataset auto-tag, S3 protocol support, model format conversion, and model one-click deploy. The project is licensed under Apache 2.0 and welcomes contributions.

The HuggingFace Model Downloader is a utility tool for downloading models and datasets from the HuggingFace website. It offers multithreaded downloading for LFS files and ensures the integrity of downloaded models with SHA256 checksum verification. The tool provides features such as nested file downloading, filter downloads for specific LFS model files, support for HuggingFace Access Token, and configuration file support. It can be used as a library or a single binary for easy model downloading and inference in projects.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

CSGHub is an open source, trustworthy large model asset management platform that can assist users in governing the assets involved in the lifecycle of LLM and LLM applications (datasets, model files, codes, etc). With CSGHub, users can perform operations on LLM assets, including uploading, downloading, storing, verifying, and distributing, through Web interface, Git command line, or natural language Chatbot. Meanwhile, the platform provides microservice submodules and standardized OpenAPIs, which could be easily integrated with users' own systems. CSGHub is committed to bringing users an asset management platform that is natively designed for large models and can be deployed On-Premise for fully offline operation. CSGHub offers functionalities similar to a privatized Huggingface(on-premise Huggingface), managing LLM assets in a manner akin to how OpenStack Glance manages virtual machine images, Harbor manages container images, and Sonatype Nexus manages artifacts.

Prompt Panel is a tool designed to accelerate the adoption of AI agents by providing a platform where users can run large language models across any inference provider, create custom agent plugins, and use their own data safely. The tool allows users to break free from walled-gardens and have full control over their models, conversations, and logic. With Prompt Panel, users can pair their data with any language model, online or offline, and customize the system to meet their unique business needs without any restrictions.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

LLMs Know More Than They Show is a repository containing code to reproduce the results in the paper. It includes scripts to generate model answers, extract exact answers, probe all layers and tokens, probe specific layers and tokens, conduct generalization experiments, perform resampling for error type probing and answer selection experiments, and run other baselines like logprob detection and p_true detection. The repository supports various datasets such as TriviaQA, Movies, HotpotQA, Winobias, Winogrande, NLI, IMDB, Math, and Natural questions. It also provides supported models like Mistral-7B-Instruct-v0.2, Mistral-7B-v0.3, Meta-Llama-3-8B, and Meta-Llama-3-8B-Instruct.

Vision-LLM-Alignment is a repository focused on implementing alignment training for visual large language models (LLMs), including SFT training, reward model training, and PPO/DPO training. It supports various model architectures and provides datasets for training. The repository also offers benchmark results and installation instructions for users.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

StarCoder2-Instruct is an open-source pipeline that introduces StarCoder2-15B-Instruct-v0.1, a self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. It generates instruction-response pairs to fine-tune StarCoder-15B without human annotations or data from proprietary LLMs. The tool is primarily finetuned for Python code generation tasks that can be verified through execution, with potential biases and limitations. Users can provide response prefixes or one-shot examples to guide the model's output. The model may have limitations with other programming languages and out-of-domain coding tasks.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. It aims to replicate the best-known performance of target model/dataset combinations in optimally-configured hardware environments. The repository will be deprecated upon the publication of v3.2.0 and will no longer be maintained or published.

This repository contains convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language (that isn't English). The rationale behind this is that LLaMa3 is trained on primarily English data and while it works to some extent for other languages, its performance is poor compared to English.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

Your friend and helper in the database world!

Takes in a Project Design Document and generates a database schema diagram for the project.

Expert in data disclosure risk analysis, mitigation, and modeling methods.

从网址提取并选择模型计算大小

Expert in generating and debugging Neo4j code, with explanations on graph database principles.

Expert in SQL queries, and Tutoring.

【AI修行中】ヘッドホンモデルに基づいたEarProfitサイズ情報提供

A detailed expert trained on all 5,588 pages of Apple CoreData, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

Convert application descriptions to consumable DB schemas or create-table SQL statements

Conversational Actuary, blending examples with technical expertise.

Just upload a numerical dataset for classification task, will apply data analysis and machine learning steps to make a best model possible.

Moved to https://chat.openai.com/g/g-KFnF7qssT-palm-reader . Interprets palm readings from user-uploaded hand images. Turned off setting to use data for OpenAi to improve model.

Moved to https://chat.openai.com/g/g-q6GNcOkYx-face-reader. Reads faces to tell fortunes based on Chinese face reading. Turned off setting to use data for OpenAi to improve model.

Java & Python programming assistant for AI model inference.

是一个专门用于建立数学模型并撰写python代码的gpt.

Apply gold-standard startup valuation and assessment methods to identify risks and gaps in your business model and product ideas.

Expert in Marian NMT models and training

Expert in ML model optimization and tuning!

Assists with ROMS and COAWST modeling in Linux environments.

A software architect for C4 Model generation.