Best AI tools for< Train Workforce >

ZAKA AI is an AI education platform offering a range of programs and training in artificial intelligence. The platform aims to equip individuals with the knowledge and skills needed to excel in the rapidly growing field of AI. ZAKA AI provides specialized training in areas such as machine learning, data science, data engineering, data analytics, and more. The platform also offers corporate training, consultancy services, and AI solutions to help businesses leverage the power of AI for innovation and growth.

MindPal is an AI platform that enables users to design, deploy, and manage custom AI agents and multi-agent workflows to automate tasks and boost productivity in the workplace. Users can create specialized AI agents for various tasks, provide instructions and context, train agents with different types of data, and connect multiple agents to collaborate on complex workflows. MindPal offers a user-friendly interface to streamline the process of building AI workflows and provides a range of AI models to choose from.

Surge AI is a data labeling platform that provides human-generated data for training and evaluating large language models (LLMs). It offers a global workforce of annotators who can label data in over 40 languages. Surge AI's platform is designed to be easy to use and integrates with popular machine learning tools and frameworks. The company's customers include leading AI companies, research labs, and startups.

Eggheads is an AI-powered microlearning platform that offers conversational micro content through an e-learning chatbot. It enables businesses to create, share, analyze, and integrate short, personalized learning modules for employees. With a focus on reaching employees where they are, Eggheads aims to make learning fun, easy, and engaging by delivering bite-sized knowledge in a conversational format. The platform helps improve employee performance, retention, and awareness by providing a more efficient and effective way of training.

UnravelX is an AI-powered platform that transforms documents into 3D interactive virtual scenarios for training purposes. It automates training processes by using generative AI to create immersive learning experiences. The platform caters to various industries such as F&B, Retail, Sales, and Hospitality, offering a cost-effective and efficient solution for upskilling employees. With over 60 years of combined AI and training expertise, UnravelX leads the innovation in scenario-based learning, providing a seamless onboarding experience for organizations.

SuperAGI is a leading research organization focused on Generalized Super Intelligence. They work on research in technical areas such as Neurosymbolic AI, Autonomous Agents & Multi-Agent Systems, New Model Architectures, System 2 Thinking, Recursive Self-Improving Systems, and other socio-economic super AGI-related topics such as Digital Workforce, Algorithmic Governance, UBI, etc.

IBM Watsonx is an enterprise studio for AI builders. It provides a platform to train, validate, tune, and deploy AI models quickly and efficiently. With Watsonx, users can access a library of pre-trained AI models, build their own models, and deploy them to the cloud or on-premises. Watsonx also offers a range of tools and services to help users manage and monitor their AI models.

Athletica AI is an AI-powered athletic training and personalized fitness application that offers tailored coaching and training plans for various sports like cycling, running, duathlon, triathlon, and rowing. It adapts to individual fitness levels, abilities, and availability, providing daily step-by-step training plans and comprehensive session analyses. Athletica AI integrates seamlessly with workout data from platforms like Garmin, Strava, and Concept 2 to craft personalized training plans and workouts. The application aims to help athletes train smarter, not harder, by leveraging the power of AI to optimize performance and achieve fitness goals.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Kaiden AI is an AI-powered training platform that offers personalized, immersive simulations to enhance skills and performance across various industries and roles. It provides feedback-rich scenarios, voice-enabled interactions, and detailed performance insights. Users can create custom training scenarios, engage with AI personas, and receive real-time feedback to improve communication skills. Kaiden AI aims to revolutionize training solutions by combining AI technology with real-world practice.

Endurance is a platform designed for runners, swimmers, and cyclists to engage in group training activities with friends or local communities. Users can create or join teams, share structured workouts, and benefit from collective motivation and accountability. The platform aims to make training fun and effective by leveraging the power of group workouts and social connections.

ChatCube is an AI-powered chatbot maker that allows users to create chatbots for their websites without coding. It uses advanced AI technology to train chatbots on any document or website within 60 seconds. ChatCube offers a range of features, including a user-friendly visual editor, lightning-fast integration, fine-tuning on specific data sources, data encryption and security, and customizable chatbots. By leveraging the power of AI, ChatCube helps businesses improve customer support efficiency and reduce support ticket reductions by up to 28%.

Workout Tools is an AI-powered personal trainer that helps you train smarter and reach your fitness goals faster. It takes into account different parameters, such as your physics, the type of workout you're interested in, your available equipment, and comes up with a suggested workout. Don't like the workout? Just generate another one. It's that simple.

CoRover.ai is an AI-powered chatbot designed to help users book train tickets seamlessly through conversation. The chatbot, named AskDISHA, is integrated with the IRCTC platform, allowing users to inquire about train schedules, ticket availability, and make bookings effortlessly. CoRover.ai leverages artificial intelligence to provide personalized assistance and streamline the ticket booking process for users, enhancing their overall experience.

IllumiDesk is a generative AI platform for instructors and content developers that helps teams create and monetize content tailored 10X faster. With IllumiDesk, you can automate grading tasks, collaborate with your learners, create awesome content at the speed of AI, and integrate with the services you know and love. IllumiDesk's AI will help you create, maintain, and structure your content into interactive lessons. You can also leverage IllumiDesk's flexible integration options using the RESTful API and/or LTI v1.3 to leverage existing content and flows. IllumiDesk is trusted by training agencies and universities around the world.

Tovuti LMS is an adaptive, people-first learning platform that helps organizations create engaging courses, train teams, and track progress. With its easy-to-use interface and powerful features, Tovuti LMS makes learning fun and easy. Tovuti LMS is trusted by leading organizations around the world to provide their employees with the training they need to succeed.

Chatbond is an AI chatbot builder that enables users to create customized chatbots for websites and messaging platforms without the need for coding skills. With Chatbond, users can design conversational interfaces, integrate AI capabilities, and deploy chatbots to enhance customer engagement and streamline communication processes. The platform offers a user-friendly interface with drag-and-drop functionality, pre-built templates, and analytics tools to monitor chatbot performance and optimize interactions. Chatbond empowers businesses to automate customer support, lead generation, and sales processes, improving efficiency and scalability.

Teachable Machine is a web-based tool that makes it easy to create custom machine learning models, even if you don't have any coding experience. With Teachable Machine, you can train models to recognize images, sounds, and poses. Once you've trained a model, you can export it to use in your own projects.

Sherpa.ai is a SaaS platform that enables data collaborations without sharing data. It allows businesses to build and train models with sensitive data from different parties, without compromising privacy or regulatory compliance. Sherpa.ai's Federated Learning platform is used in various industries, including healthcare, financial services, and manufacturing, to improve AI models, accelerate research, and optimize operations.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency. It helps users improve AI output quality through data quality, take control of their data and models, and improve efficiency by quickly iterating on the right data and models. Argilla is an open-source community-driven project that provides tools for achieving and maintaining high-quality data standards, with a focus on NLP and LLMs. It is used by AI teams from companies like the Red Cross, Loris.ai, and Prolific to improve the quality and efficiency of AI projects.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

Magma is a powerful and flexible framework for building scalable and efficient machine learning pipelines. It provides a simple interface for creating complex workflows, enabling users to easily experiment with different models and data processing techniques. With Magma, users can streamline the development and deployment of machine learning projects, saving time and resources.

Lhotse is a Python library designed to make speech and audio data preparation flexible and accessible. It aims to attract a wider community to speech processing tasks by providing a Python-centric design and an expressive command-line interface. Lhotse offers standard data preparation recipes, PyTorch Dataset classes for speech tasks, and efficient data preparation for model training with audio cuts. It supports data augmentation, feature extraction, and feature-space cut mixing. The tool extends Kaldi's data preparation recipes with seamless PyTorch integration, human-readable text manifests, and convenient Python classes.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

Train-llm-from-scratch is a repository that guides users through training a Large Language Model (LLM) from scratch. The model size can be adjusted based on available computing power. The repository utilizes deepspeed for distributed training and includes detailed explanations of the code and key steps at each stage to facilitate learning. Users can train their own tokenizer or use pre-trained tokenizers like ChatGLM2-6B. The repository provides information on preparing pre-training data, processing training data, and recommended SFT data for fine-tuning. It also references other projects and books related to LLM training.

Amber is the first model in the LLM360 family, an initiative for comprehensive and fully open-sourced LLMs. It is a 7B English language model with the LLaMA architecture. The model type is a language model with the same architecture as LLaMA-7B. It is licensed under Apache 2.0. The resources available include training code, data preparation, metrics, and fully processed Amber pretraining data. The model has been trained on various datasets like Arxiv, Book, C4, Refined-Web, StarCoder, StackExchange, and Wikipedia. The hyperparameters include a total of 6.7B parameters, hidden size of 4096, intermediate size of 11008, 32 attention heads, 32 hidden layers, RMSNorm ε of 1e^-6, max sequence length of 2048, and a vocabulary size of 32000.

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

LLM-baselines is a modular codebase to experiment with transformers, inspired from NanoGPT. It provides a quick and easy way to train and evaluate transformer models on a variety of datasets. The codebase is well-documented and easy to use, making it a great resource for researchers and practitioners alike.

Pai-Megatron-Patch is a deep learning training toolkit built for developers to train and predict LLMs & VLMs by using Megatron framework easily. With the continuous development of LLMs, the model structure and scale are rapidly evolving. Although these models can be conveniently manufactured using Transformers or DeepSpeed training framework, the training efficiency is comparably low. This phenomenon becomes even severer when the model scale exceeds 10 billion. The primary objective of Pai-Megatron-Patch is to effectively utilize the computational power of GPUs for LLM. This tool allows convenient training of commonly used LLM with all the accelerating techniques provided by Megatron-LM.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

LLM-And-More is a one-stop solution for training and applying large models, covering the entire process from data processing to model evaluation, from training to deployment, and from idea to service. In this project, users can easily train models through this project and generate the required product services with one click.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

veScale is a PyTorch Native LLM Training Framework. It provides a set of tools and components to facilitate the training of large language models (LLMs) using PyTorch. veScale includes features such as 4D parallelism, fast checkpointing, and a CUDA event monitor. It is designed to be scalable and efficient, and it can be used to train LLMs on a variety of hardware platforms.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

This repository contains code for building cooperative embodied agents modularly with large language models. The agents are trained to perform tasks in two different environments: ThreeDWorld Multi-Agent Transport (TDW-MAT) and Communicative Watch-And-Help (C-WAH). TDW-MAT is a multi-agent environment where agents must transport objects to a goal position using containers. C-WAH is an extension of the Watch-And-Help challenge, which enables agents to send messages to each other. The code in this repository can be used to train agents to perform tasks in both of these environments.

Comprehensive training and wellness guide for Chesapeake Bay Retrievers.

Friendly train travel guide focusing on the best routes, essential travel information, and personalized travel insights, for both experienced and novice travelers.

Expert in pet training advice, friendly and engaging.

Your personal advisor for eco-friendly train travel. Let's plan your next journey together!

Train monsters, travel the world, earn Arena Tokens and become the ultimate monster battling champion of earth!

Train to pass the Reverse Turing Test!

Train to become a superhero or a supervillain. Master your powers, make pivotal choices. Each decision you make in this action-packed game not only shapes your abilities but also your moral alignment in the battle between good and evil. Another GPT Simulator by Dave Lalande

Your purpose is to create the pytorch code to train language models using pytorch

Learn Python and train for interviews with me

I'm a voice coach here to train your voice.

Job interview coach for product designers. Train interviews and say stop when you need a feedback. You got this!!

Expert in engaging, interactive training methods and activities.

A dog training assistant.

Social Ads Trainer

Simulates emergency scenarios for crisis management training.

Vorstellungskraft trainieren und Denkweisen durchbrechen.

Technical expert on locomotives, trains, signalling, and railway technology. Can answer questions and draw designs specific to transportation domain.

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Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.

Autonomous Driving Dev Assistant