Best AI tools for< Train For Civil Service Exam >

ChatGPT is an AI-powered chatbot tool that allows users to create and train chatbots for various purposes. It uses advanced natural language processing to generate human-like responses and engage in conversations with users. With ChatGPT, you can customize the chatbot's responses, train it on specific topics, and integrate it into your website or application to provide interactive and personalized experiences for your users.

ResolveAI is a platform that allows users to create and train AI agents for customer service. These agents can be used to automate tasks such as answering FAQs, scheduling meetings, and collecting leads. ResolveAI's agents are trained using a variety of sources, including documents, website pages, and live data sources. Once trained, agents can be customized to fit the user's brand and integrated with a variety of platforms, including websites, social media, and messaging apps.

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.

Practical Deep Learning for Coders is a free course designed for individuals with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. The course covers topics such as building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems. It is based on a 5-star rated book and does not require any special hardware or software. The course is led by Jeremy Howard, a renowned expert in machine learning and the President and Chief Scientist of Kaggle.

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.

ChatFast is a platform that allows businesses to create custom GPT chatbots using their own data. These chatbots can be used to answer customer questions, capture leads, and schedule appointments. ChatFast is easy to use and requires no coding. It is trusted by thousands of businesses and provides a range of powerful features, including the ability to train chatbots on multiple data sources, revise responses, capture leads, and create smart forms.

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.

Google for Developers provides developers with tools, resources, and documentation to build apps for Android, Chrome, ChromeOS, Cloud, Firebase, Flutter, Google AI Studio, Google Maps Platform, Google Workspace, TensorFlow, and YouTube. It also offers programs and events for developers to learn and connect with each other.

Wonderchat is an AI chatbot builder that allows you to create a custom chatbot using your business data. You can build a chatbot in 5 minutes that can answer customer support queries, provide information about your products or services, and more. Wonderchat is easy to use, even if you don't have any coding experience. You can embed your chatbot on your website or use it on messaging platforms like Facebook Messenger and WhatsApp.

Threado is an AI application designed for customer service, offering AI agents trained on internal knowledge to provide instant, automated support to customers and internal teams. It integrates effortlessly into daily tools like Slack and Microsoft Teams, allowing users to access company information instantly. Threado AI helps resolve customer queries, automate chat support, personalize customer engagement, accelerate sales processes, and transform how customer-facing teams find information and get work done.

Artiko.ai is a multi-model AI chat platform that integrates advanced AI models such as ChatGPT, Claude 3, Gemini 1.5, and Mistral AI. It offers a convenient and cost-effective solution for work, business, or study by providing a single chat interface to harness the power of multi-model AI. Users can save time and money while achieving better results through features like text rewriting, data conversation, AI assistants, website chatbot, PDF and document chat, translation, brainstorming, and integration with various tools like Woocommerce, Amazon, Salesforce, and more.

Chatness AI is an AI-powered chatbot assistant application that serves as an Intercom alternative, helping businesses enhance customer service and reduce costs. It allows users to automate tasks, sales, and marketing, create custom chat tools, and integrate with various platforms like Discord, Facebook Messenger, Google Calendar, and WhatsApp. With features such as support for anonymous and authenticated users, lead collection, custom automations, and comprehensive branding customizations, Chatness AI offers a seamless and efficient solution for businesses looking to streamline their customer support processes.

The AI Seed Phrase Finder & BTC balance checker tool for Windows PC is an innovative application designed to prevent the loss of access to Bitcoin wallets. Leveraging advanced algorithms and artificial intelligence techniques, this program efficiently analyzes vast amounts of data to pre-train AI models. Consequently, it generates and searches for mnemonic phrases that grant access to abandoned Bitcoin wallets holding nonzero balances. With the “AI Seed Finder tool for Windows PC”, locating a complete 12-word seed phrase for a specific Bitcoin wallet becomes effortless. Even if you possess only partial knowledge of the mnemonic phrase or individual words comprising it, this tool can swiftly identify the entire seed phrase. Furthermore, by providing the address of a specific Bitcoin wallet you wish to regain access to, the program narrows down the search area. This targeted approach significantly enhances the program’s efficiency and reduces the time required to ascertain the correct mnemonic phrase.

般若AI is an AI-powered platform that provides a variety of AI tools and services to help businesses and individuals with their AI needs. The platform offers a range of features, including AI training, AI consulting, and AI development. 般若AI also has a large community of AI experts who can provide support and guidance to users.

Metaflow is an open-source framework for building and managing real-life ML, AI, and data science projects. It makes it easy to use any Python libraries for models and business logic, deploy workflows to production with a single command, track and store variables inside the flow automatically for easy experiment tracking and debugging, and create robust workflows in plain Python. Metaflow is used by hundreds of companies, including Netflix, 23andMe, and Realtor.com.

Mo Ai Jobs is a job board for artificial intelligence (AI) professionals. It lists jobs in machine learning, engineering, research, data science, and other AI-related fields. The site is designed to help AI professionals find jobs at next-generation AI companies. Mo Ai Jobs is a valuable resource for anyone looking for a job in the AI industry.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

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.

Weights & Biases is a machine learning platform that helps data scientists and engineers build, train, and deploy machine learning models. It provides a central location to track and manage all of your machine learning projects, and it offers a variety of tools to help you collaborate with others and share your work.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

The 'awesome-mobile-robotics' repository is a curated list of important content related to Mobile Robotics and AI. It includes resources such as courses, books, datasets, software and libraries, podcasts, conferences, journals, companies and jobs, laboratories and research groups, and miscellaneous resources. The repository covers a wide range of topics in the field of Mobile Robotics and AI, providing valuable information for enthusiasts, researchers, and professionals in the domain.

Construction-Hazard-Detection is an AI-driven tool focused on improving safety at construction sites by utilizing the YOLOv8 model for object detection. The system identifies potential hazards like overhead heavy loads and steel pipes, providing real-time analysis and warnings. Users can configure the system via a YAML file and run it using Docker. The primary dataset used for training is the Construction Site Safety Image Dataset enriched with additional annotations. The system logs are accessible within the Docker container for debugging, and notifications are sent through the LINE messaging API when hazards are detected.

Detoxify is a library that provides trained models and code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. It includes models like 'original', 'unbiased', and 'multilingual' trained on different datasets to detect toxicity and minimize bias. The library aims to help in stopping harmful content online by interpreting visual content in context. Users can fine-tune the models on carefully constructed datasets for research purposes or to aid content moderators in flagging out harmful content quicker. The library is built to be user-friendly and straightforward to use.

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.

LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

Gemini is an open-source model designed to handle multiple modalities such as text, audio, images, and videos. It utilizes a transformer architecture with special decoders for text and image generation. The model processes input sequences by transforming them into tokens and then decoding them to generate image outputs. Gemini differs from other models by directly feeding image embeddings into the transformer instead of using a visual transformer encoder. The model also includes a component called Codi for conditional generation. Gemini aims to effectively integrate image, audio, and video embeddings to enhance its performance.

Boxcars is a Ruby gem that enables users to create new systems with AI composability, incorporating concepts such as LLMs, Search, SQL, Rails Active Record, Vector Search, and more. It allows users to work with Boxcars, Trains, Prompts, Engines, and VectorStores to solve problems and generate text results. The gem is designed to be user-friendly for beginners and can be extended with custom concepts. Boxcars is actively seeking ways to enhance security measures to prevent malicious actions. Users can use Boxcars for tasks like running calculations, performing searches, generating Ruby code for math operations, and interacting with APIs like OpenAI, Anthropic, and Google SERP.

FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.

The 'fms-fsdp' repository is a companion to the Foundation Model Stack, providing a (pre)training example to efficiently train FMS models, specifically Llama2, using native PyTorch features like FSDP for training and SDPA implementation of Flash attention v2. It focuses on leveraging FSDP for training efficiently, not as an end-to-end framework. The repo benchmarks training throughput on different GPUs, shares strategies, and provides installation and training instructions. It trained a model on IBM curated data achieving high efficiency and performance metrics.

Bert-VITS2 is a repository that provides a backbone with multilingual BERT for text-to-speech (TTS) applications. It offers an alternative to BV2/GSV projects and is inspired by the MassTTS project. Users can refer to the code to learn how to train models for TTS. The project is not maintained actively in the short term. It is not to be used for any purposes that violate the laws of the People's Republic of China, and strictly prohibits any political-related use.

The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

This repository contains the reinforcement learning training code for Trackmania AI with Reinforcement Learning. It is a research work-in-progress project that aims to apply reinforcement learning principles to play Trackmania. The code is constantly evolving and may not be clean or easily usable. The training hyperparameters are intentionally changed in the public repository to encourage understanding of reinforcement learning principles. The project may not receive active support for setup or usage at the moment.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

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