Best AI tools for< Build Vocabulary >

Lucida AI is an AI-driven coaching tool designed to enhance employees' English language skills through personalized insights and feedback based on real-life call interactions. The tool offers comprehensive coaching in pronunciation, fluency, grammar, vocabulary, and tracking of language proficiency. It provides advanced speech analysis using proprietary LLM and NLP technologies, ensuring accurate assessments and detailed tracking. With end-to-end encryption for data privacy, Lucy AI is a cost-effective solution for organizations seeking to improve communication skills and streamline language assessment processes.

Tutor AI is an AI English-speaking application designed to assist individuals in practicing their spoken English skills with the aid of an artificial intelligence chatbot. The app offers a safe and judgment-free environment for users to engage in free-flowing, natural conversations with diverse AI characters. It provides real-time feedback, suggests better ways to express oneself, and offers adjustable features to enhance the learning experience. Tutor AI aims to improve users' spoken English skills confidently and effectively through personalized lessons and interactive learning.

This is an AI-powered language learning app that helps you grow your vocabulary in Spanish. It uses a variety of methods, including learning by example and repetition, to help you learn new words and phrases quickly and easily.

The website is currently displaying a '403 Forbidden' error, which means that access to the requested resource is forbidden. This error is typically caused by insufficient permissions or server misconfiguration. The 'openresty' mentioned in the text is likely the web server software being used. It is not an AI tool or application, but rather a web server technology.

LanguaTalk is a language learning platform that offers personalized coaching from 5-star tutors and world-class AI software to help users achieve fluency in various languages. The platform provides tailored language tutoring, practice sessions with advanced AI, and a combination of both for accelerated learning. LanguaTalk leverages AI technology to offer features such as personalized coaching, practice conversations, role plays, instant corrections, interactive transcripts, vocabulary learning through flashcards, and more. The platform has both free and paid plans, with a focus on providing effective and affordable language learning solutions.

Speak is a language learning app that focuses on improving speaking skills through interaction with an advanced AI language tutor. The app provides personalized curriculum, on-the-go conversational practice, and motivation to help users achieve fluency quickly. With a 4.8 rating and over 5 million downloads, Speak offers a versatile and interactive platform for language learners of all levels.

Speak is a language learning app that uses AI to help you improve your speaking skills. It offers a variety of features, including personalized lessons, instant feedback, and a virtual tutor. Speak is designed to be fun and engaging, and it can help you learn a new language quickly and easily.

Scarlett Panda is a customized short stories app for children that uses AI to generate unique and personalized stories in 30 seconds. The app offers a variety of features, including physical personalized books, instant stories, storybook meditation, lullabies, learning adventures, and illustrations. Scarlett Panda is designed to help children expand their vocabulary, engage with reading, and build magical memories with their parents. The app is also available in 74 languages.

Learn Languages AI is a language learning tool that uses artificial intelligence to help users learn new languages. The tool is built on Telegram and allows users to speak, text, and play with an AI teacher. Learn Languages AI is designed to help users reach all of their language learning goals. The tool is free to use and does not require an account.

Learn Languages AI is an AI-powered language learning application that allows users to practice conversational language skills with an AI teacher. Users can speak, text, and play with the AI teacher to achieve their language learning goals. The application is built on Telegram platform, offering a seamless and user-friendly experience. With no account required, users can start learning immediately. Join over 1000 happy users from various countries who are learning languages such as German, Polish, Spanish, Italian, French, Dutch, Brazilian Portuguese, Indian, and Chinese. Created by @franzstupar, the developer of the renowned #1 AI Cover Letter Generator.

Yomitai is a Japanese reading assistant that helps learners of Japanese to read and understand Japanese text. It provides a variety of features to help learners, including a built-in dictionary, grammar checker, and text-to-speech functionality. Yomitai is available as a web application and as a mobile app for iOS and Android.

This is a unified DevOps platform to build AI applications. It provides a comprehensive set of tools and services to help developers build, deploy, and manage AI applications. The platform includes a variety of features such as a code editor, a debugger, a profiler, and a deployment manager. It also provides access to a variety of AI services, such as natural language processing, machine learning, and computer vision.

Build Chatbot is a no-code chatbot builder designed to simplify the process of creating chatbots. It enables users to build their chatbot without any coding knowledge, auto-train it with personalized content, and get the chatbot ready with an engaging UI. The platform offers various features to enhance user engagement, provide personalized responses, and streamline communication with website visitors. Build Chatbot aims to save time for both businesses and customers by making information easily accessible and transforming visitors into satisfied customers.

The website 'What should I build next?' is a platform designed to help developers generate random development project ideas. It serves as an ultimate resource for developers seeking inspiration for their next project. Users can pick components or randomize to create unique project ideas. The platform offers free credits to active users daily and encourages user engagement. With a user-friendly interface, the website aims to support developers in overcoming creative blocks and kickstarting new projects.

Google Cloud is a suite of cloud computing services that runs on the same infrastructure as Google. Its services include computing, storage, networking, databases, machine learning, and more. Google Cloud is designed to make it easy for businesses to develop and deploy applications in the cloud. It offers a variety of tools and services to help businesses with everything from building and deploying applications to managing their infrastructure. Google Cloud is also committed to sustainability, and it has a number of programs in place to reduce its environmental impact.

Bubble is a no-code application development platform that allows users to build and deploy web and mobile applications without writing any code. It provides a visual interface for designing and developing applications, and it includes a library of pre-built components and templates that can be used to accelerate development. Bubble is suitable for a wide range of users, from beginners with no coding experience to experienced developers who want to build applications quickly and easily.

Gemini is a large and powerful AI model developed by Google. It is designed to handle a wide variety of text and image reasoning tasks, and it can be used to build a variety of AI-powered applications. Gemini is available in three sizes: Ultra, Pro, and Nano. Ultra is the most capable model, but it is also the most expensive. Pro is the best performing model for a wide variety of tasks, and it is a good value for the price. Nano is the most efficient model, and it is designed for on-device use cases.

FlutterFlow is a low-code development platform that enables users to build cross-platform mobile and web applications without writing code. It provides a visual interface for designing user interfaces, connecting data, and implementing complex logic. FlutterFlow is trusted by users at leading companies around the world and has been used to build a wide range of applications, from simple prototypes to complex enterprise solutions.

Whimsical is an iterative workspace designed for product teams to collaborate effectively. It offers a range of tools such as flowcharts, wireframes, mind maps, and documentation features to help teams visualize ideas, streamline processes, and create a shared understanding. With Whimsical, users can generate diagrams quickly, brainstorm and organize ideas visually, and build wireframes with ease. The platform promotes clarity, collaboration, and efficiency in product development projects.

Zyro is a website builder that allows users to create professional websites and online stores without any coding knowledge. It offers a range of features, including customizable templates, drag-and-drop editing, and AI-powered tools to help users brand and grow their businesses.

Tiny LLM zh is a project aimed at building a small-parameter Chinese language large model for quick entry into learning large model-related knowledge. The project implements a two-stage training process for large models and subsequent human alignment, including tokenization, pre-training, instruction fine-tuning, human alignment, evaluation, and deployment. It is deployed on ModeScope Tiny LLM website and features open access to all data and code, including pre-training data and tokenizer. The project trains a tokenizer using 10GB of Chinese encyclopedia text to build a Tiny LLM vocabulary. It supports training with Transformers deepspeed, multiple machine and card support, and Zero optimization techniques. The project has three main branches: llama2_torch, main tiny_llm, and tiny_llm_moe, each with specific modifications and features.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

Chinese-Mixtral-8x7B is an open-source project based on Mistral's Mixtral-8x7B model for incremental pre-training of Chinese vocabulary, aiming to advance research on MoE models in the Chinese natural language processing community. The expanded vocabulary significantly improves the model's encoding and decoding efficiency for Chinese, and the model is pre-trained incrementally on a large-scale open-source corpus, enabling it with powerful Chinese generation and comprehension capabilities. The project includes a large model with expanded Chinese vocabulary and incremental pre-training code.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

This repository provides an LLM-driven audio codec model, LLM-Codec, for building multi-modal LLMs (text and audio modalities). The model enables frozen LLMs to achieve multiple audio tasks in a few-shot style without parameter updates. It compresses the audio modality into a well-trained LLMs token space, treating audio representation as a 'foreign language' that LLMs can learn with minimal examples. The proposed approach supports tasks like speech emotion classification, audio classification, text-to-speech generation, speech enhancement, etc., demonstrating feasibility and effectiveness in simple scenarios. The LLM-Codec model is open-sourced to facilitate research on few-shot audio task learning and multi-modal LLMs.

Bark.cpp is a C/C++ implementation of the Bark model, a real-time, multilingual text-to-speech generation model. It supports AVX, AVX2, and AVX512 for x86 architectures, and is compatible with both CPU and GPU backends. Bark.cpp also supports mixed F16/F32 precision and 4-bit, 5-bit, and 8-bit integer quantization. It can be used to generate realistic-sounding audio from text prompts.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

COLD-Attack is a framework designed for controllable jailbreaks on large language models (LLMs). It formulates the controllable attack generation problem and utilizes the Energy-based Constrained Decoding with Langevin Dynamics (COLD) algorithm to automate the search of adversarial LLM attacks with control over fluency, stealthiness, sentiment, and left-right-coherence. The framework includes steps for energy function formulation, Langevin dynamics sampling, and decoding process to generate discrete text attacks. It offers diverse jailbreak scenarios such as fluent suffix attacks, paraphrase attacks, and attacks with left-right-coherence.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

Mergekit is a toolkit for merging pre-trained language models. It uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

DataDreamer is an advanced toolkit designed to facilitate the development of edge AI models by enabling synthetic data generation, knowledge extraction from pre-trained models, and creation of efficient and potent models. It eliminates the need for extensive datasets by generating synthetic datasets, leverages latent knowledge from pre-trained models, and focuses on creating compact models suitable for integration into any device and performance for specialized tasks. The toolkit offers features like prompt generation, image generation, dataset annotation, and tools for training small-scale neural networks for edge deployment. It provides hardware requirements, usage instructions, available models, and limitations to consider while using the library.

mllm is a fast and lightweight multimodal LLM inference engine for mobile and edge devices. It is a Plain C/C++ implementation without dependencies, optimized for multimodal LLMs like fuyu-8B, and supports ARM NEON and x86 AVX2. The engine offers 4-bit and 6-bit integer quantization, making it suitable for intelligent personal agents, text-based image searching/retrieval, screen VQA, and various mobile applications without compromising user privacy.

Facilitates learning foreign words with visual mnemonics

Specializing in Peppa Pig script-based echolalia support

Teaches GRE vocab through personalized stories and thoughtful questions, featuring GregMat

Guides you through IELTS speaking tests

Your friendly English practice partner.

Expert in all kinds of PC builds.

街を作ろう　街を作ろう　変わらぬ鼓動　弾ませろよ (作者: @_saip_)

Guides to Build a Successful Network State

Unique custom images based on your input. Just type ideas and the brand image is created.

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Your tech buddy helping you refurbish or build a PC from scratch, tailored to your needs, budget, and language.

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Help you build browser extensions more efficiently.

Your expert in Gradle build configuration, offering clear, practical advice.