Best AI tools for< Reformat Function >

EarnBetter is an AI-powered platform that offers assistance in creating professional resumes, cover letters, and job search support. The platform utilizes artificial intelligence to rewrite and reformat resumes, generate tailored cover letters, provide personalized job matches, and offer interview support. Users can upload their current resume to get started and access a range of features to enhance their job search process. EarnBetter aims to streamline the job search experience by providing free, unlimited, and professional document creation services.

gptel-aibo is an AI writing assistant system built on top of gptel. It helps users create and manage content in Emacs, including code, documentation, and novels. Users can interact with the Language Model (LLM) to receive suggestions and apply them easily. The tool provides features like sending requests, applying suggestions, and completing content at the current position based on context. Users can customize settings and face settings for a better user experience. gptel-aibo aims to enhance productivity and efficiency in content creation and management within Emacs environment.

GraphLLM is a graph-based framework designed to process data using LLMs. It offers a set of tools including a web scraper, PDF parser, YouTube subtitles downloader, Python sandbox, and TTS engine. The framework provides a GUI for building and debugging graphs with advanced features like loops, conditionals, parallel execution, streaming of results, hierarchical graphs, external tool integration, and dynamic scheduling. GraphLLM is a low-level framework that gives users full control over the raw prompt and output of models, with a steeper learning curve. It is tested with llama70b and qwen 32b, under heavy development with breaking changes expected.

The Spring AI project provides a Spring-friendly API and abstractions for developing AI applications. It offers a portable client API for interacting with generative AI models, enabling developers to easily swap out implementations and access various models like OpenAI, Azure OpenAI, and HuggingFace. Spring AI also supports prompt engineering, providing classes and interfaces for creating and parsing prompts, as well as incorporating proprietary data into generative AI without retraining the model. This is achieved through Retrieval Augmented Generation (RAG), which involves extracting, transforming, and loading data into a vector database for use by AI models. Spring AI's VectorStore abstraction allows for seamless transitions between different vector database implementations.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

veRL is a flexible and efficient reinforcement learning training framework designed for large language models (LLMs). It allows easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructures, and flexible device mapping. The framework achieves state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine. It supports popular HuggingFace models and is suitable for users working with PyTorch FSDP, Megatron-LM, and vLLM backends.

Kairon is an open-source conversational digital transformation platform that helps build LLM-based digital assistants at scale. It provides a no-coding web interface for adapting, training, testing, and maintaining AI assistants. Kairon focuses on pre-processing data for chatbots, including question augmentation, knowledge graph generation, and post-processing metrics. It offers end-to-end lifecycle management, low-code/no-code interface, secure script injection, telemetry monitoring, chat client designer, analytics module, and real-time struggle analytics. Kairon is suitable for teams and individuals looking for an easy interface to create, train, test, and deploy digital assistants.

The 'gritlm' repository provides all materials for the paper Generative Representational Instruction Tuning. It includes code for inference, training, evaluation, and known issues related to the GritLM model. The repository also offers models for embedding and generation tasks, along with instructions on how to train and evaluate the models. Additionally, it contains visualizations, acknowledgements, and a citation for referencing the work.

The LLM Prompt Tuning Playbook is a comprehensive guide for improving the performance of post-trained Language Models (LLMs) through effective prompting strategies. It covers topics such as pre-training vs. post-training, considerations for prompting, a rudimentary style guide for prompts, and a procedure for iterating on new system instructions. The playbook emphasizes the importance of clear, concise, and explicit instructions to guide LLMs in generating desired outputs. It also highlights the iterative nature of prompt development and the need for systematic evaluation of model responses.

Anki AI Utils is a powerful suite of AI-powered tools designed to enhance your Anki flashcard learning experience by automatically improving cards you struggle with. The tools include features such as adaptive learning, personalized memory hooks, automation readiness, universal compatibility, provider agnosticism, and infinite extensibility. The toolkit consists of tools like Illustrator for creating custom mnemonic images, Reformulator for rephrasing flashcards, Mnemonics Creator for generating memorable mnemonics, Explainer for providing detailed explanations, and Mnemonics Helper for quick mnemonic generation. The project aims to motivate others to package the tools into addons for wider accessibility.

The Octopus-v4 project aims to build the world's largest graph of language models, integrating specialized models and training Octopus models to connect nodes efficiently. The project focuses on identifying, training, and connecting specialized models. The repository includes scripts for running the Octopus v4 model, methods for managing the graph, training code for specialized models, and inference code. Environment setup instructions are provided for Linux with NVIDIA GPU. The Octopus v4 model helps users find suitable models for tasks and reformats queries for effective processing. The project leverages Language Large Models for various domains and provides benchmark results. Users are encouraged to train and add specialized models following recommended procedures.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

KnowAgent is a tool designed for Knowledge-Augmented Planning for LLM-Based Agents. It involves creating an action knowledge base, converting action knowledge into text for model understanding, and a knowledgeable self-learning phase to continually improve the model's planning abilities. The tool aims to enhance agents' potential for application in complex situations by leveraging external reservoirs of information and iterative processes.

nanoPerplexityAI is an open-source implementation of a large language model service that fetches information from Google. It involves a simple architecture where the user query is checked by the language model, reformulated for Google search, and an answer is generated and saved in a markdown file. The tool requires minimal setup and is designed for easy visualization of answers.

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

The Raycast AI Proxy is a tool that acts as a proxy for the Raycast AI application, allowing users to utilize the application without subscribing. It intercepts and forwards Raycast requests to various AI APIs, then reformats the responses for Raycast. The tool supports multiple AI providers and allows for custom model configurations. Users can generate self-signed certificates, add them to the system keychain, and modify DNS settings to redirect requests to the proxy. The tool is designed to work with providers like OpenAI, Azure OpenAI, Google, and more, enabling tasks such as AI chat completions, translations, and image generation.