Best AI tools for< Recommend Training Method >

Eightfold Talent Intelligence is an AI platform that offers a comprehensive suite of solutions for talent acquisition, talent management, workforce exchange, and resource management. Powered by deep-learning AI and global talent data sets, the platform helps organizations realize the full potential of their workforce by providing skills-driven insights and enabling better talent decisions. From finding and developing talent to matching employees with the right opportunities, Eightfold's AI technology revolutionizes the world of work by connecting people with possibilities.

Enthral is an AI-powered Learning Management System (LMS) and Learning Experience Platform (LXP) that offers personalized and integrated learning experiences for employees and extended enterprise. It leverages AI to recommend relevant content, bridge skill gaps, and automate tasks, empowering users to curate their learning journeys. Enthral caters to various industries and use cases, providing seamless mobile learning, assessments, certifications, gamification, and social learning features. The platform focuses on building a future-ready workforce through continuous skill development and data-driven decision-making.

Gift Recommender is an AI-powered application designed to assist users in finding the perfect gift for their loved ones. By providing basic information about the recipient such as name, age, gender, price range, and interests, the AI generates personalized gift recommendations. The system learns from user feedback to continuously improve its suggestions. While the AI provides recommendations, it acknowledges that the best gift is often something personal and encourages users to provide feedback for better training.

AI Chatbot Hub is a no-code AI chatbot platform that allows users to create AI agents quickly and easily. Users can build AI chatbots in minutes, customize appearance, track every chat with variables and labels, and integrate the chatbots anywhere. The platform offers features like lead collection, dynamic webhooks, file upload, human support, auto chatbot re-train, function calling, training files, sources and citations, and fine-tune intents. AI Chatbot Hub is suitable for various industries such as customer support, real estate, healthcare, restaurants, e-commerce, insurance, and contractors. It offers flexible pricing plans catering to small businesses and growing brands, with features like unlimited messages, AI agents, storage space, collaborators, tokens per chatbot, AI models, variables, conversation labels, and more.

Support AI is a custom AI chatbot application powered by ChatGPT that allows website owners to create personalized chatbots to provide instant answers to customers, capture leads, and enhance customer support. With Support AI, users can easily integrate AI chatbots on their websites, train them with specific content, and customize their behavior and responses. The application offers features such as capturing leads, providing accurate answers, handling bookings, collecting feedback, and offering product recommendations. Users can choose from different pricing plans based on their message volume and training content needs.

The One-Stop Natural Language Hotel Recommender is an AI-powered tool that simplifies the process of finding the perfect hotel for your needs. By utilizing natural language processing technology, the tool can understand your preferences and requirements, and provide you with personalized hotel recommendations. It considers factors such as proximity to popular places, top-rated establishments, summarized reviews, and staying within your budget. With this tool, you can easily find the ideal accommodation for your next trip without the hassle of extensive research.

Prefixbox is an AI-powered search and discovery solution that offers a range of features to enhance user experience and boost revenue for Enterprise retailers. It provides personalized search, rich autocomplete, navigation, recommendations, insights, and analytics. Prefixbox is trusted by various industries and offers easy integration, localized language support, and market-driven development. The application helps in increasing conversion rates, average order value, and revenue through data-driven search results and relevant recommendations.

LavieTaste.AI is an AI-powered application that helps users explore and discover the best restaurants offering Singaporean and Japanese cuisine. By simply entering the desired food or place, the tool provides personalized recommendations for top dining experiences. Users can find a variety of options ranging from retro cafes to popular buffets and top steak houses. LavieTaste.AI aims to enhance the culinary journey of users by offering tailored suggestions based on their preferences and location.

AI Bookstore is a website that uses AI to help users find books that they want to read. Users can ask the AI questions about what they are looking for, and the AI will recommend books that match their criteria. The AI can also generate personalized recommendations based on a user's reading history.

Remove Objects from Photos - AI Image Eraser tool hama.app is an online tool that allows you to remove unwanted objects from your photos with just a few clicks. It uses artificial intelligence to automatically detect and remove objects, making it easy to clean up your photos and get rid of anything you don't want. With hama.app, you can remove people, objects, blemishes, and even entire backgrounds from your photos, leaving you with a clean and polished image.

Octane AI is a powerful AI tool designed for Shopify stores to create smart quizzes that drive revenue growth. It offers a no-code interface, seamless integrations with platforms like Shopify and Klaviyo, personalized product recommendations, and automated email marketing. With features like conditional logic, advanced design options, and in-depth analytics, Octane AI helps businesses engage customers, collect insights, and personalize the shopping journey. The platform is built for ecommerce marketers by ecommerce marketers, with a focus on increasing sales, boosting conversions, and fostering stronger customer relationships.

BuildShip.com is a powerful AI Assistant Builder that allows users to create AI Assistant ChatBots in just 5 minutes. The platform enables users to connect to tools and databases without the need for any code, offering full flexibility with low-code options. Users can build AI Assistants using popular models like OpenAI, Claude 3, and Azure, and can easily ship their creations as APIs, chat widgets, workflow, or schedule jobs. The platform also provides secure integration with databases, the ability to generate custom action nodes, and seamless plugin chat widgets for websites. BuildShip.com simplifies the process of building AI Assistants and empowers users to bring their ideas to life effortlessly.

Monoid is an AI tool that transforms APIs into AI Agents, enabling Large Language Models (LLMs) to act in real-time with relevant context. Users can create customizable Agents in minutes, simulate AI Agents using APIs, chat with Agents, and share Actions on the Hub. Monoid offers use-cases like Shopping Assistant, Customer Support Agent, and Workflow Automator to help businesses streamline processes and enhance customer interactions.

FINIITE AI is a retail and brand marketing solution that uses AI to help businesses grow their sales and improve customer experience. The company's flagship product is a product recommendation engine that uses AI to personalize product recommendations for each customer. FINIITE AI also offers a suite of other AI-powered solutions, including a skin check tool, a customer data insights platform, and a CRM integration. FINIITE AI's solutions are used by businesses of all sizes, from small businesses to large enterprises.

Pinecone is a vector database designed to build knowledgeable AI applications. It offers a serverless platform with high capacity and low cost, enabling users to perform low-latency vector search for various AI tasks. Pinecone is easy to start and scale, allowing users to create an account, upload vector embeddings, and retrieve relevant data quickly. The platform combines vector search with metadata filters and keyword boosting for better application performance. Pinecone is secure, reliable, and cloud-native, making it suitable for powering mission-critical AI applications.

Nosto is an AI-powered ecommerce personalization platform that offers a suite of solutions for driving sales, fostering loyalty, and creating memorable shopping experiences. The platform leverages artificial intelligence, business intelligence, and dynamic audience segmentation to deliver intelligent commerce experiences that increase revenue. Nosto's Experience Clouds enable users to turn commerce data into online revenue by providing enterprise-grade personalization, search and discovery, and user-generated content modules.

The website, eightfold.ai, is an AI tool that offers solutions for talent acquisition and management. It leverages artificial intelligence to streamline the recruitment process, match candidates with suitable roles, and enhance workforce diversity. By utilizing advanced algorithms and machine learning, eightfold.ai aims to revolutionize how organizations discover, engage, and retain talent. The platform provides a comprehensive suite of features to optimize hiring practices and improve employee experiences.

Shaped is a cloud-based platform that provides APIs and tools for building and deploying ranking systems. It offers a variety of features to help developers quickly and easily create and manage ranking models, including a multi-connector SQL interface, a real-time feature store, and a library of pre-built models. Shaped is designed to be scalable, cost-efficient, and easy to use, making it a great option for businesses of all sizes.

VideaHealth is a dental AI platform trusted by dentists and DSOs. It enhances diagnostics and streamlines workflows using clinical AI to identify and convert treatments across major oral conditions. The platform combines practice management system data with AI insights to elevate patient care and empower dental practices. VideaHealth offers advanced FDA-cleared detection algorithms to detect suspect diseases, provides AI-powered insights for data-driven decisions, and delivers real-time chairside assistance to dentists.

Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings. Vectors power most of what you already do online - hailing a cab, finding a funny video, getting a date, scrolling through a feed or paying with a tap. And yet, building production systems powered by vectors is still too hard! Our goal is to help enterprises put vectors at the center of their data & compute infrastructure, to build smarter and more reliable software.

Colossal-AI is a deep learning system for large-scale parallel training. It provides a unified interface to scale sequential code of model training to distributed environments. Colossal-AI supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

Rubra is a collection of open-weight large language models enhanced with tool-calling capability. It allows users to call user-defined external tools in a deterministic manner while reasoning and chatting, making it ideal for agentic use cases. The models are further post-trained to teach instruct-tuned models new skills and mitigate catastrophic forgetting. Rubra extends popular inferencing projects for easy use, enabling users to run the models easily.

Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.

Generative Models by Stability AI is a repository that provides various generative models for research purposes. It includes models like Stable Video 4D (SV4D) for video synthesis, Stable Video 3D (SV3D) for multi-view synthesis, SDXL-Turbo for text-to-image generation, and more. The repository focuses on modularity and implements a config-driven approach for building and combining submodules. It supports training with PyTorch Lightning and offers inference demos for different models. Users can access pre-trained models like SDXL-base-1.0 and SDXL-refiner-1.0 under a CreativeML Open RAIL++-M license. The codebase also includes tools for invisible watermark detection in generated images.

Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates tasks such as data cleaning, preprocessing, and complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. It aims to expedite the data analysis process, making it accessible to all, regardless of their expertise in data analysis. The tool is built to empower users to process data and achieve high-quality visualizations with unparalleled efficiency, and to execute high-performance modeling with the best strategies. Future enhancements include Natural Language Processing (NLP), neural networks, and object detection utilizing YOLO, broadening its capabilities to meet diverse data analysis needs.

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 implements multiple key-value cache pruning methods and benchmarks using transformers, aiming to simplify the development of new methods for researchers and developers in the field of long-context language models. It provides a set of 'presses' that compress the cache during the pre-filling phase, with each press having a compression ratio attribute. The repository includes various training-free presses, special presses, and supports KV cache quantization. Users can contribute new presses and evaluate the performance of different presses on long-context datasets.

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.

Deej-A.I. is an advanced machine learning project that aims to revolutionize music recommendation systems by using artificial intelligence to analyze and recommend songs based on their content and characteristics. The project involves scraping playlists from Spotify, creating embeddings of songs, training neural networks to analyze spectrograms, and generating recommendations based on similarities in music features. Deej-A.I. offers a unique approach to music curation, focusing on the 'what' rather than the 'how' of DJing, and providing users with personalized and creative music suggestions.

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.

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.

This repository contains the code for coding, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In _Build a Large Language Model (From Scratch)_, you'll discover how LLMs work from the inside out. In this book, I'll guide you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.

**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.

Only Train Once (OTO) is an automatic, architecture-agnostic DNN training and compression framework that allows users to train a general DNN from scratch or a pretrained checkpoint to achieve high performance and slimmer architecture simultaneously in a one-shot manner without fine-tuning. The framework includes features for automatic structured pruning and erasing operators, as well as hybrid structured sparse optimizers for efficient model compression. OTO provides tools for pruning zero-invariant group partitioning, constructing pruned models, and visualizing pruning and erasing dependency graphs. It supports the HESSO optimizer and offers a sanity check for compliance testing on various DNNs. The repository also includes publications, installation instructions, quick start guides, and a roadmap for future enhancements and collaborations.

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