Best AI tools for< Use Instruction Tuning Data >

LessonPlans.ai is an AI-powered lesson plan generator that helps teachers create high-quality lesson plans in seconds. With this tool, teachers can easily generate detailed, personalized lesson plans that are tailored to their students' needs. LessonPlans.ai also includes a step-by-step guide for each lesson, making it easy for teachers to follow and implement the plan in their classrooms.

Class Companion is an AI teaching assistant tool designed to provide instant coaching and AI feedback to students on their assignments. It helps improve student engagement and outcomes by offering multiple attempts, targeted help, and personalized feedback. The tool supports various subjects and grades, allowing teachers to save time on manual feedback and focus on lesson planning and individual instruction. With features like AI-generated content, in-depth reporting, and customizable rubrics, Class Companion aims to enhance student learning and comprehension.

Canopy Directory is an AI tool designed specifically for educators. It provides a comprehensive directory of AI tools that can be used in educational settings. The platform aims to streamline the process of finding and utilizing AI tools for teaching and learning purposes. Educators can explore a wide range of tools categorized based on their functionalities and applications, making it easier to integrate AI technology into their teaching practices. Canopy Directory serves as a valuable resource for educators looking to enhance their teaching methods through the use of AI tools.

Google Gemma is a lightweight, state-of-the-art open language model (LLM) developed by Google. It is part of the same research used in the creation of Google's Gemini models. Gemma models come in two sizes, the 2B and 7B parameter versions, where each has a base (pre-trained) and instruction-tuned modifications. Gemma models are designed to be cross-device compatible and optimized for Google Cloud and NVIDIA GPUs. They are also accessible through Kaggle, Hugging Face, Google Cloud with Vertex AI or GKE. Gemma models can be used for a variety of applications, including text generation, summarization, RAG, and both commercial and research use.

Monsha is an AI tool designed for teachers to assist with curriculum planning, resource creation, and differentiation. It serves as an all-in-one hub for teachers, offering features such as lesson planning, resource generation, and assessment creation. Monsha aims to simplify the teaching workflow by leveraging advanced AI models to automate tasks and provide educators with ready-to-use teaching materials. The platform allows teachers to customize, adapt, and export resources in various formats, making it a valuable tool for educators worldwide.

DapperGPT is a user interface (UI) for ChatGPT that provides a better user experience and additional features. It offers an intuitive interface, AI-powered notes, a Chrome extension, smart search, the ability to pin favorites, image generation, character instruction prompts, and code generation. DapperGPT is free to use, but requires a valid OpenAI API key. Premium features are also available for purchase, which include additional customization options and cloud sync.

Fly AI is a powerful and user-friendly AI application designed for Mac users. It leverages the capabilities of OpenAI's ChatGPT to provide fast responses, unlimited custom instructions, and quick access. With a beautiful design and context-aware features, Fly AI allows users to work faster and smarter by creating custom AI mini-apps. The application prioritizes user privacy by not recording data and offering a standalone subscription model. Fly AI is a native Mac app, ensuring a seamless user experience without the need to open a browser.

WP Dev AI is an AI-powered tool that allows users to build custom features for their WordPress websites without having to code. With WP Dev AI, users can simply describe the feature they want to create in plain English, and the tool will generate the necessary code. WP Dev AI also provides step-by-step instructions on how to implement the code, making it easy for even non-technical users to add custom features to their websites.

16x Prompt is a desktop application that helps developers compose prompts for coding tasks in ChatGPT. It simplifies prompt creation by adding context, source code, and formatting instructions. The app supports all major programming languages and frameworks, and it can be used to generate prompts for a variety of coding tasks, including coding from scratch, debugging, refactoring, and more. 16x Prompt is free to download and use, and it can be used with both ChatGPT and GPT-4.

AIBookTools is an AI-powered application designed to help users turn their bookshelf wisdom into actionable insights. The tool guides users through implementing frameworks from popular books on various scenarios using AI technology. It offers a range of frameworks derived from best-selling books to transform reading into practical strategies for personal growth, productivity, leadership, business strategy, financial planning, and lifelong learning. With a user-friendly interface and detailed instructions, AIBookTools aims to make reading more impactful and efficient for its users.

ChatGPT Writer is a free browser extension that helps you write emails, messages, and other text-based content using the power of AI. It uses the ChatGPT AI language model to generate high-quality, natural-sounding text that can save you time and effort. With ChatGPT Writer, you can quickly and easily create professional emails, social media posts, website content, and more. The extension is easy to use and works on all websites. Simply highlight the text you want to rewrite or generate, and then click the ChatGPT Writer icon. A command box will appear where you can enter your instructions. ChatGPT Writer will then generate the new text based on your instructions.

Bash Senpai is a terminal assistant powered by ChatGPT that transforms instructions into ready-to-use commands. It provides convenience by allowing users to get answers without leaving the terminal and offers better answers by providing context with questions. The tool also incorporates self-reflection to improve the quality of its responses.

教えてAI byGMO is a Japanese portal site for generating prompts (instructions) for ChatGPT and Gemini-like generative AI. Users can input their desired tasks and receive prompt suggestions to use with AI tools. The site aims to provide a platform for creating effective prompts for communication with AI models, enabling users to easily interact with and utilize generative AI technology.

Sora is an AI-powered video creation tool that allows users to create realistic and imaginative videos from text instructions. It is powered by OpenAI's Gemini 1.5 Pro, a large language model with 10 million tokens. Sora is designed to be easy to use, even for those with no video editing experience. Users simply need to enter a text prompt describing the video they want to create, and Sora will generate a video based on that prompt.

Luma AI Video Generator is an AI model designed to create high-quality and fantastical videos from text instructions and images. It offers fast video generation, realistic motion and cinematography, physical accuracy and consistency, diverse camera movements, and scalability. Users can quickly generate videos by inputting text descriptions, and the tool is free to use with a limited free quota per month.

BrowseGPT is a free Chrome extension that uses artificial intelligence to automate your browser. You can give BrowseGPT instructions like "Find a place to stay in Seattle on February 22nd" or "buy a children's book on Amazon", and it will use OpenAI's GPT-3 model to process web pages and issue commands like CLICK, ENTER_TEXT, or NAVIGATE to complete the task for you.

The website offers 'Q, ChatGPT for Slack', an AI tool that functions like ChatGPT within your Slack workspace. It allows on-demand URL and file reading, custom instructions for tailored use, and supports various URLs and files. With Q, users can summarize, evaluate, brainstorm ideas, self-review, engage in Q&A, and more. The tool enables team-specific rules, guidelines, and templates, making it ideal for emails, translations, content creation, copywriting, reporting, coding, and testing based on internal information.

Mentals.ai is an operating system for AI agents that enables users to design and deploy complex AI agents using natural language instructions. It focuses on creativity and logic rather than programming, allowing for multi-agent collaboration, advanced features like memory and code execution, and various use cases such as business automation, personal automation, and marketing automation. The platform also supports the creation of intelligent chatbots, virtual employees, and multi-agent systems, revolutionizing productivity and problem-solving in various domains.

SendPulse offers AI chatbots for messengers that can provide automated responses and help users solve a wide range of tasks. These chatbots are powered by GPT-3 technology from OpenAI, which allows them to generate text and code, and simulate human managers. You can use these chatbots in Telegram, Facebook Messenger, Instagram, and WhatsApp. They can be configured to pause when a manager joins the chat, and you can provide instructions for the AI operation by adding plain text. No coding skills are required.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Cambrian-1 is a fully open project focused on exploring multimodal Large Language Models (LLMs) with a vision-centric approach. It offers competitive performance across various benchmarks with models at different parameter levels. The project includes training configurations, model weights, instruction tuning data, and evaluation details. Users can interact with Cambrian-1 through a Gradio web interface for inference. The project is inspired by LLaVA and incorporates contributions from Vicuna, LLaMA, and Yi. Cambrian-1 is licensed under Apache 2.0 and utilizes datasets and checkpoints subject to their respective original licenses.

K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.

LLM-Synthetic-Data is a repository focused on real-time, fine-grained LLM-Synthetic-Data generation. It includes methods, surveys, and application areas related to synthetic data for language models. The repository covers topics like pre-training, instruction tuning, model collapse, LLM benchmarking, evaluation, and distillation. It also explores application areas such as mathematical reasoning, code generation, text-to-SQL, alignment, reward modeling, long context, weak-to-strong generalization, agent and tool use, vision and language, factuality, federated learning, generative design, and safety.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

This repository contains the code for the paper 'LESS: Selecting Influential Data for Targeted Instruction Tuning'. The work proposes a data selection method to choose influential data for inducing a target capability. It includes steps for warmup training, building the gradient datastore, selecting data for a task, and training with the selected data. The repository provides tools for data preparation, data selection pipeline, and evaluation of the model trained on the selected data.

Bonito is an open-source model for conditional task generation, converting unannotated text into task-specific training datasets for instruction tuning. It is a lightweight library built on top of Hugging Face `transformers` and `vllm` libraries. The tool supports various task types such as question answering, paraphrase generation, sentiment analysis, summarization, and more. Users can easily generate synthetic instruction tuning datasets using Bonito for zero-shot task adaptation.

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

LLamaTuner is a repository for the Efficient Finetuning of Quantized LLMs project, focusing on building and sharing instruction-following Chinese baichuan-7b/LLaMA/Pythia/GLM model tuning methods. The project enables training on a single Nvidia RTX-2080TI and RTX-3090 for multi-round chatbot training. It utilizes bitsandbytes for quantization and is integrated with Huggingface's PEFT and transformers libraries. The repository supports various models, training approaches, and datasets for supervised fine-tuning, LoRA, QLoRA, and more. It also provides tools for data preprocessing and offers models in the Hugging Face model hub for inference and finetuning. The project is licensed under Apache 2.0 and acknowledges contributions from various open-source contributors.

SoM-LLaVA is a new data source and learning paradigm for Multimodal LLMs, empowering open-source Multimodal LLMs with Set-of-Mark prompting and improved visual reasoning ability. The repository provides a new dataset that is complementary to existing training sources, enhancing multimodal LLMs with Set-of-Mark prompting and improved general capacity. By adding 30k SoM data to the visual instruction tuning stage of LLaVA, the tool achieves 1% to 6% relative improvements on all benchmarks. Users can train SoM-LLaVA via command line and utilize the implementation to annotate COCO images with SoM. Additionally, the tool can be loaded in Huggingface for further usage.

EasyInstruct is a Python package proposed as an easy-to-use instruction processing framework for Large Language Models (LLMs) like GPT-4, LLaMA, ChatGLM in your research experiments. EasyInstruct modularizes instruction generation, selection, and prompting, while also considering their combination and interaction.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

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