Best AI tools for< Build Tokenizer >

Basis Theory is a platform that helps businesses build a fully programmable vault for creating engaging commerce flows, connecting with partners, managing compliance effortlessly, and maintaining control over payments data. It offers flexible payment solutions, industry-tailored payment flows, and custom payment strategies for various use cases. The platform is designed to cater to high-risk merchants, subscription platforms, marketplaces, fintechs, and more, providing full control over customer card data and tailored payment experiences.

Ocean Protocol is a tokenized AI and data platform that enables users to monetize AI models and data while maintaining privacy. It offers tools like Predictoor for running AI-powered prediction bots, Ocean Nodes for enhancing AI capabilities, and features like Data NFTs and Datatokens for protecting intellectual property and controlling data access. The platform focuses on decentralized AI, privacy, and modular architecture to empower users in the AI and data science domains.

Deckee.AI is an AI-powered platform that allows users to instantly build blockchain websites and tokens. With Deckee.AI, users can create customized webpages for blogging, consulting, digital creation, and more. Deckee.AI also provides powerful editing tools, domain and SSL, separate hosting options, and the ability to choose the exact layout users want. Additionally, Deckee.AI makes it easy to create professional designs and digital collections, as well as unique digital tokens as a representation of products, events, rewards, and more.

Toolblox is an AI-powered platform that enables users to create purpose-built, audited smart-contracts and Dapps for tokenized assets quickly and efficiently. It offers a no-code solution for turning ideas into smart-contracts, visualizing workflows, and creating tokenization solutions. With pre-audited smart-contracts, examples, and an AI assistant, Toolblox simplifies the process of building and launching decentralized applications. The platform caters to founders, agencies, and businesses looking to streamline their operations and leverage blockchain technology.

Atriv is a comprehensive digital art creation and monetization platform that empowers artists to showcase, sell, and earn from their creations. With a user-friendly interface and advanced tools, Atriv provides a seamless experience for artists to create stunning digital art, connect with collectors, and build a sustainable income stream.

Tresata is an AI tool that offers inventory and cataloging, inferencing and connecting, discoverability and lineage tracking, tokenization, and data enrichment capabilities. It provides SAM (Smart Augmented Intelligence) features and seamless integrations for customers. The platform empowers users to create data products for AI applications by uploading data to the Tresata cloud and accessing it for analysis and insights. Tresata emphasizes the importance of good data for all, with a focus on data-driven decision-making and innovation.

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.

Apify is a full-stack web scraping and data extraction platform that allows developers to build, deploy, and publish web scraping, data extraction, and web automation tools. The platform offers ready-made web scraping tools, serverless programs, integrations with various apps and services, storage for results, anti-blocking proxies, and an open-source web scraping and crawling library. Apify simplifies web scraping challenges by providing a full-stack platform that includes tools for developing, deploying, and running web scrapers efficiently.

Durable is an AI-powered website builder and small business software platform that offers tools for building business websites, managing customers, creating blogs, and establishing brand identity. With Durable, users can effortlessly create professional websites in seconds without the need for coding skills. The platform also provides resources, advice, and industry-specific guides to help businesses succeed. Durable's AI tools aim to simplify the process of starting, growing, and managing a business, offering features like SEO optimization, marketing tools, and review automation.

Pinecone is a vector database that helps power AI for the world's best companies. It is a serverless database that lets you deliver remarkable GenAI applications faster, at up to 50x lower cost. Pinecone is easy to use and can be integrated with your favorite cloud provider, data sources, models, frameworks, and more.

Cursor is an AI-first code editor that helps developers build software faster. It provides a variety of features to help developers, including code completion, code generation, and error detection. Cursor is also designed to be easy to use and integrates with popular development tools like VSCode.

Abacus.AI is the world's first AI platform where AI, not humans, build Applied AI agents and systems at scale. Using generative AI and other novel neural net techniques, AI can build LLM apps, gen AI agents, and predictive applied AI systems at scale.

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.

code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.

Репозиторий содержит информацию о применении искусственного интеллекта в разработке программного обеспечения. В частности, рассматриваются кейсы использования ChatGPT и других языковых моделей для автоматизации задач разработки, таких как написание кода, тестирование, рефакторинг и генерация документации.

BaoGPT is an AI project designed to facilitate asking questions about YouTube videos. It features a web UI based on Gradio and Discord integration. The tool utilizes a pipeline that routes input questions to either a greeting-like branch or a query & answer branch. The query analysis is performed by the LLM, which extracts attributes as filters and optimizes and rewrites questions for better vector retrieval in the vector DB. The tool then retrieves top-k candidates for grading and outputs final relative documents after grading. Lastly, the LLM performs summarization based on the reranking output, providing answers and attaching sources to the user.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

This repository provides all the necessary items to build a Language Model from scratch, inspired by Karpathy's nanoGPT and Shakespeare generator. It includes data collection tools, data processing scripts, various models like BERT, GPT, and Seq-2-Seq, along with tokenizer and training files.

JavaScript tokenizer for LLaMA 3 designed for client-side use in the browser and Node, with TypeScript support. It accurately calculates token count, has 0 dependencies, optimized running time, and somewhat optimized bundle size. Compatible with most LLaMA 3 models. Can encode and decode text, but training is not supported. Pollutes global namespace with `llama3Tokenizer` in the browser. Mostly compatible with LLaMA 3 models released by Facebook in April 2024. Can be adapted for incompatible models by passing custom vocab and merge data. Handles special tokens and fine tunes. Developed by belladore.ai with contributions from xenova, blaze2004, imoneoi, and ConProgramming.

This repository aims to build a low-parameter LLM model through pretraining, fine-tuning, model rewarding, and reinforcement learning stages to create a chat model capable of simple conversation tasks. It features using the bert4torch training framework, seamless integration with transformers package for inference, optimized file reading during training to reduce memory usage, providing complete training logs for reproducibility, and the ability to customize robot attributes. The chat model supports multi-turn conversations. The trained model currently only supports basic chat functionality due to limitations in corpus size, model scale, SFT corpus size, and quality.

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.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

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.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

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

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

The stable-diffusion.cpp repository provides an implementation for inferring stable diffusion in pure C/C++. It offers features such as support for different versions of stable diffusion, lightweight and dependency-free implementation, various quantization support, memory-efficient CPU inference, GPU acceleration, and more. Users can download the built executable program or build it manually. The repository also includes instructions for downloading weights, building from scratch, using different acceleration methods, running the tool, converting weights, and utilizing various features like Flash Attention, ESRGAN upscaling, PhotoMaker support, and more. Additionally, it mentions future TODOs and provides information on memory requirements, bindings, UIs, contributors, and references.

AI Samples for .NET is a repository containing various samples demonstrating how to use AI in .NET applications. It provides quickstarts using Semantic Kernel and Azure OpenAI SDK, covers LLM Core Concepts, End to End Examples, Local Models, Local Embedding Models, Tokenizers, Vector Databases, and Reference Examples. The repository showcases different AI-related projects and tools for developers to explore and learn from.

Starwhale is an MLOps/LLMOps platform that brings efficiency and standardization to machine learning operations. It streamlines the model development lifecycle, enabling teams to optimize workflows around key areas like model building, evaluation, release, and fine-tuning. Starwhale abstracts Model, Runtime, and Dataset as first-class citizens, providing tailored capabilities for common workflow scenarios including Models Evaluation, Live Demo, and LLM Fine-tuning. It is an open-source platform designed for clarity and ease of use, empowering developers to build customized MLOps features tailored to their needs.

OpenShield is a firewall designed for AI models to protect against various attacks such as prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency granting, overreliance, and model theft. It provides rate limiting, content filtering, and keyword filtering for AI models. The tool acts as a transparent proxy between AI models and clients, allowing users to set custom rate limits for OpenAI endpoints and perform tokenizer calculations for OpenAI models. OpenShield also supports Python and LLM based rules, with upcoming features including rate limiting per user and model, prompts manager, content filtering, keyword filtering based on LLM/Vector models, OpenMeter integration, and VectorDB integration. The tool requires an OpenAI API key, Postgres, and Redis for operation.

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.

Your Personal Super Car Wallpaper Creator

Helps you build an OpenAI Assistant Client

Build extensions using the Eyeware Beam eye tracking SDK

Advice and guidance on how to build, maintain, and improve friendships.

Build Contents in Seconds

Business Model Canvas Creator - Build and evaluate your business model

Build your own League of Legends Style Champion with Abilities, Back Story and Splash Art

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

Helps build full stack apps with React.

Help you build browser extensions more efficiently.

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

This GPT will build app & answer questions for flipper zero.

Use AI to build an online business

Build on the XRP Ledger with assistance from this GPT trained on extensive documentation and code samples.

Build new undergraduate degrees with ease

The #1 open source GPT to build your SKAN 4.0 conversion values schema.