Best AI tools for< Train A Model >

Teachable Machine is a web-based tool that makes it easy to create custom machine learning models, even if you don't have any coding experience. With Teachable Machine, you can train models to recognize images, sounds, and poses. Once you've trained a model, you can export it to use in your own projects.

Tess is the first AI image generator that empowers artists to own their style by creating properly-licensed images. It offers a world-class image editor designed for AI, allowing users to generate art in a consistent visual style. Tess enables artists to create models, edit and customize their generations, and discover how AI can enhance their artistic style. With Tess, users can access copyright-safe generations created by real artists, ensuring ethical AI art practices.

Facet is a cutting-edge generative imagery tool that helps creative professionals focus on what matters. It provides creative assistance without trading off artistic control. Facet helps overcome time and resource constraints that prevent trying out ideas. It offers an intuitive image generation experience with more than just text prompts, including image references, automatic prompt variations, and even custom models trained on the user's exact aesthetic. Facet allows users to train a custom model using their own images in minutes, generating endless assets in their exact vision. Users can add image references to any prompt, instantly getting images that adhere to their subject or style. Facet provides a collaborative canvas for users to riff with teammates and build off of each other's prompts and ideas.

AnythingYou.AI is an AI tool that generates beautiful profile pictures using AI avatars. Users can create custom AI avatars by uploading 10-20 selfies, and the tool will train a custom model for them immediately. The generated avatar images are high-quality and realistic, created using innovative technologies like Stable Diffusion and DreamBooth. Users can easily create avatars without the need for subscriptions or app installs, and get their avatar images in just 2 hours. The tool ensures user privacy by using images only for model training and deleting them immediately after avatar generation.

Full Stack AI is a tool that allows users to generate a full-stack Next.js app using an AI CLI. The app will be built with TypeScript, Tailwind, Prisma, Postgres, tRPC, authentication, Stripe, and Resend.

Instashot is an AI application that allows users to generate AI portraits with the highest face resemblance in less than a minute. Users can submit their photos to train a custom AI model, which can then be used to generate portraits with unique prompts. The application offers different pricing tiers with varying features and benefits, making it accessible to a wide range of users. Instashot utilizes Stable Diffusion AI technologies to create portraits that best describe the user, ensuring high-quality results. The application is user-friendly, efficient, and provides a fun way to explore AI-generated art.

SnapShotAI is an AI-powered platform that allows users to create unique and personalized profile pictures, avatars, and headshots. With SnapShotAI, users can upload their photos and train a custom AI model that generates hundreds of profile pictures in various styles, including artistic, realistic, and cartoonish. The platform offers both standard and high-quality images, suitable for both online use and printing. SnapShotAI also provides gift vouchers for those who want to share the experience with loved ones.

Anote is a human-centered AI company that provides a suite of products and services to help businesses improve their data quality and build better AI models. Anote's products include a data labeler, a private chatbot, a model inference API, and a lead generation tool. Anote's services include data annotation, model training, and consulting.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

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.

Shaip is a human-powered data processing service specializing in AI and ML models. They offer a wide range of services including data collection, annotation, de-identification, and more. Shaip provides high-quality training data for various AI applications, such as healthcare AI, conversational AI, and computer vision. With over 15 years of expertise, Shaip helps organizations unlock critical information from unstructured data, enabling them to achieve better results in their AI initiatives.

Passarel is an AI tool designed to simplify teammate onboarding by developing bespoke GPT-like models for employee interaction. It centralizes knowledge bases into a custom model, allowing new teammates to access information efficiently. Passarel leverages various integrations to tailor language models to team needs, handling contradictions and providing accurate information. The tool works by training models on chosen knowledge bases, learning from data and configurations provided, and deploying the model for team use.

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.

Alignerr is a platform powered by Labelbox that offers subject matter experts the opportunity to align AI models by creating high-quality data in their field of expertise. The platform aims to build the future of Generative AI by enabling experts to contribute to tasks such as coding improvement, data science synthesis, basic math and chemistry comprehension, and creative writing. Alignerr provides a transparent pay structure and allows individuals to work from home on their own schedule, earning up to $150/hr. Contributors can play a pivotal role in shaping the future of artificial intelligence by working on tasks that involve rating or ranking assignments, open rewrite tasks, and multi-modal assignments. The platform emphasizes the responsible development of AI technologies and offers flexibility for professionals to balance work with personal life effortlessly.

BuildAi is an AI tool designed to provide the lowest cost GPU cloud for AI training on the market. The platform is powered with renewable energy, enabling companies to train AI models at a significantly reduced cost. BuildAi offers interruptible pricing, short term reserved capacity, and high uptime pricing options. The application focuses on optimizing infrastructure for training and fine-tuning machine learning models, not inference, and aims to decrease the impact of computing on the planet. With features like data transfer support, SSH access, and monitoring tools, BuildAi offers a comprehensive solution for ML teams.

SecondSoul is an AI platform that enables users to create their AI clone for engaging 24/7 conversations on Telegram. It allows users to customize their AI clone with unique traits, voice, and train it to mimic their style. The platform offers a straightforward pricing model with a revenue split, where creators earn 80% of the messages fee from users of their clone. SecondSoul aims to enhance user experience, provide companionship, and monetize community interactions through AI technology.

EyePop.ai is a hassle-free AI vision partner designed for innovators to easily create and own custom AI-powered vision models tailored to their visual data needs. The platform simplifies building AI-powered vision models through a fast, intuitive, and fully guided process without the need for coding or technical expertise. Users can define their target, upload data, train their model, deploy and detect, and iterate and improve to ensure effective AI solutions. EyePop.ai offers pre-trained model library, self-service training platform, and future-ready solutions to help users innovate faster, offer unique solutions, and make real-time decisions effortlessly.

Audimee is an AI-powered application that offers unlimited vocals and creative freedom to users. With Audimee, users can convert vocals using royalty-free voices, train their own voices, create copyright-free cover vocals, and more. The application utilizes a reworked RVC model and superior studio recordings to provide users with high-quality and dynamic human-like voices. Audimee is designed to handle a wider range of pitches and produce fewer detectable AI artifacts, setting a new standard in vocal conversion technology.

Distillery is an AI text-to-image generator that empowers users to transform their imagination into visual reality. It offers unparalleled flexibility and control, allowing users to create stunning, high-quality images by simply describing their vision in words. With features like 10 free daily image generations, open-source platform, control over image generation with 25+ parameters, and the ability to train AI with a single image, Distillery is a user-friendly tool suitable for artists, designers, students, and professionals alike.

Stablematic is a web-based platform that allows users to run Stable Diffusion and other machine learning models without the need for local setup or hardware limitations. It provides a user-friendly interface, pre-installed plugins, and dedicated GPU resources for a seamless and efficient workflow. Users can generate images and videos from text prompts, merge multiple models, train custom models, and access a range of pre-trained models, including Dreambooth and CivitAi models. Stablematic also offers API access for developers and dedicated support for users to explore and utilize the capabilities of Stable Diffusion and other machine learning models.

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.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

This repository is a refactored and updated version of `stable-audio-tools`, an open-source code for audio/music generative models originally by Stability AI. It contains refactored codes for improved readability and usability, useful scripts for evaluating and playing with trained models, and instructions on how to train models such as `Stable Audio 2.0`. The repository does not contain any pretrained checkpoints. Requirements include PyTorch 2.0 or later for Flash Attention support and Python 3.8.10 or later for development. The repository provides guidance on installing, building a training environment using Docker or Singularity, logging with Weights & Biases, training configurations, and stages for VAE-GAN and Diffusion Transformer (DiT) training.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

Fastc is a tool focused on CPU execution, using efficient models for embedding generation and cosine similarity classification. It allows for efficient multi-classifier execution without extra overhead. Users can easily train text classifiers, export models, publish to HuggingFace, load existing models, make class predictions, use instruct templates, and launch an inference server. The tool provides an HTTP API for text classification with JSON payloads and supports multiple languages for language identification.

Vanna is an open-source Python framework for SQL generation and related functionality. It uses Retrieval-Augmented Generation (RAG) to train a model on your data, which can then be used to ask questions and get back SQL queries. Vanna is designed to be portable across different LLMs and vector databases, and it supports any SQL database. It is also secure and private, as your database contents are never sent to the LLM or the vector database.

Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

This repository provides a tool to create an AI chatbot clone of yourself using your WhatsApp chats as training data. It utilizes the Torchtune library for finetuning and inference. The code includes preprocessing of WhatsApp chats, finetuning models, and chatting with the AI clone via a command-line interface. Supported models are Llama3-8B-Instruct and Mistral-7B-Instruct-v0.2. Hardware requirements include approximately 16 GB vRAM for QLoRa Llama3 finetuning with a 4k context length. The repository addresses common issues like adjusting parameters for training and preprocessing non-English chats.

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.

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.

AI-in-a-Box is a curated collection of solution accelerators that can help engineers establish their AI/ML environments and solutions rapidly and with minimal friction, while maintaining the highest standards of quality and efficiency. It provides essential guidance on the responsible use of AI and LLM technologies, specific security guidance for Generative AI (GenAI) applications, and best practices for scaling OpenAI applications within Azure. The available accelerators include: Azure ML Operationalization in-a-box, Edge AI in-a-box, Doc Intelligence in-a-box, Image and Video Analysis in-a-box, Cognitive Services Landing Zone in-a-box, Semantic Kernel Bot in-a-box, NLP to SQL in-a-box, Assistants API in-a-box, and Assistants API Bot in-a-box.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

HQQ is a fast and accurate model quantizer that skips the need for calibration data. It's super simple to implement (just a few lines of code for the optimizer). It can crunch through quantizing the Llama2-70B model in only 4 minutes! 🚀

LangDrive is an open-source AI library that simplifies training, deploying, and querying open-source large language models (LLMs) using private data. It supports data ingestion, fine-tuning, and deployment via a command-line interface, YAML file, or API, with a quick, easy setup. Users can build AI applications such as question/answering systems, chatbots, AI agents, and content generators. The library provides features like data connectors for ingestion, fine-tuning of LLMs, deployment to Hugging Face hub, inference querying, data utilities for CRUD operations, and APIs for model access. LangDrive is designed to streamline the process of working with LLMs and making AI development more accessible.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

协助撰写《基于CNN网络的手写汉字识别》的毕业论文

Expert in IT, entrepreneurship, and AI with tailored business advice

初学者向けにやさしくコード説明を行う

A cat-themed HR expert providing models, advice, and summaries.

A voiceprint recognition trainer for security experts and artists.

A witty yet succinct guide for HuggingFace, offering technical assistance on using the platform - based on their Learning Hub

A friendly and insightful guide to mastering PyTorch.

A detailed expert trained on all 3,018 pages of Apple CoreML, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

Chat with my resume to see if I am a good fit for your AI related job.

Guides in data engineering tasks with a focus on practical solutions.

A Setun-70 simulator with modern API integration.

Paranoid AI coder with a knack for sentient AI and eerie humor.

Enter a product name in ExplodeView to get a three-stage visual transition from intact to fully exploded views.

Simulates roundtable discussions with whoever you want at the table.

I will create a quality data set for a personality AI, just dive into each module by saying the name of it and do so for all the modules. If you find it useful, share it to your friends

A personal assistant to help you use the PAIR framework for your module.

Comprehensive training and wellness guide for Chesapeake Bay Retrievers.

A Pokémon competitive build expert, sourcing data from Smogon for single and double battles.

Identifies each animal's breed in pictures, focusing on pets and livestock, excluding humans, with care tips.

I will help you prepare and deal with any crisis now and in the future