Best AI tools for< Train Multi-modal 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.

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.

SuperAGI is a leading research organization focused on Generalized Super Intelligence. They work on research in technical areas such as Neurosymbolic AI, Autonomous Agents & Multi-Agent Systems, New Model Architectures, System 2 Thinking, Recursive Self-Improving Systems, and other socio-economic super AGI-related topics such as Digital Workforce, Algorithmic Governance, UBI, etc.

Cerebras is an AI tool that offers products and services related to AI supercomputers, cloud system processors, and applications for various industries. It provides high-performance computing solutions, including large language models, and caters to sectors such as health, energy, government, scientific computing, and financial services. Cerebras specializes in AI model services, offering state-of-the-art models and training services for tasks like multi-lingual chatbots and DNA sequence prediction. The platform also features the Cerebras Model Zoo, an open-source repository of AI models for developers and researchers.

Tavus is an AI tool that offers digital twin APIs for video generation and conversational video interfaces. It provides developers with cutting-edge AI technology to create immersive video experiences using AI-generated digital twins. Tavus' Phoenix model enables the generation of realistic digital replicas with natural face movements and expressions. The platform also supports rapid training, instant inference, and multi-language capabilities. With a developer-first approach, Tavus focuses on security, trust, and user experience, offering features like dubbing APIs and automated content moderation. The tool is praised for its speed of development cycles, high-quality AI video, and exceptional customer service.

Roboflow is an AI tool designed for computer vision tasks, offering a platform that allows users to annotate, train, deploy, and perform inference on models. It provides integrations, ecosystem support, and features like notebooks, autodistillation, and supervision. Roboflow caters to various industries such as aerospace, agriculture, healthcare, finance, and more, with a focus on simplifying the development and deployment of computer vision models.

Azure AI Platform by Microsoft offers a comprehensive suite of artificial intelligence services and tools for developers and businesses. It provides a unified platform for building, training, and deploying AI models, as well as integrating AI capabilities into applications. With a focus on generative AI, multimodal models, and large language models, Azure AI empowers users to create innovative AI-driven solutions across various industries. The platform also emphasizes content safety, scalability, and agility in managing AI projects, making it a valuable resource for organizations looking to leverage AI technologies.

Cartesia Sonic Team Blog Research Playground is an AI application that offers real-time multimodal intelligence for every device. The application aims to build the next generation of AI by providing ubiquitous, interactive intelligence that can run on any device. It features the fastest, ultra-realistic generative voice API and is backed by research on simple linear attention language models and state-space models. The founding team, who met at the Stanford AI Lab, has invented State Space Models (SSMs) and scaled it up to achieve state-of-the-art results in various modalities such as text, audio, video, images, and time-series data.

V7 is an AI data engine for computer vision and generative AI. It provides a multimodal automation tool that helps users label data 10x faster, power AI products via API, build AI + human workflows, and reach 99% AI accuracy. V7's platform includes features such as automated annotation, DICOM annotation, dataset management, model management, image annotation, video annotation, document processing, and labeling services.

AIxBlock is an AI tool that empowers users to unleash their AI initiatives on the Blockchain. The platform offers a comprehensive suite of features for building, deploying, and monitoring AI models, including AI data engine, multimodal-powered data crawler, auto annotation, consensus-driven labeling, MLOps platform, decentralized marketplaces, and more. By harnessing the power of blockchain technology, AIxBlock provides cost-efficient solutions for AI builders, compute suppliers, and freelancers to collaborate and benefit from decentralized supercomputing, P2P transactions, and consensus mechanisms.

Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.

DocsAI is an AI-powered document companion that helps you organize, search, and chat with your documents. It integrates with various sources, including websites, text files, PDFs, Docx, Notion, and Confluence. You can customize the companion's appearance to match your brand and suggest better answers to improve its accuracy. DocsAI also offers a chat widget that can be embedded on any website, allowing you to chat with your documents and get summaries, insights, and leads. It is mobile and tablet-friendly, and you can export chats and analyze data to identify trends and improve customer satisfaction. DocsAI is open source and offers custom prompts and multi-language support.

Petal is a document analysis platform powered by generative AI technology. It allows users to chat with their documents, providing fully sourced and reliable answers by linking to their own knowledge bases. Users can train AI on their documents to support their work, ensuring centralized knowledge management and document synchronization. Petal offers features such as automatic metadata extraction, file deduplication, and collaboration tools to enhance productivity and streamline workflows for researchers, faculty, and industry experts.

Synthesis AI is a synthetic data platform that enables more capable and ethical computer vision AI. It provides on-demand labeled images and videos, photorealistic images, and 3D generative AI to help developers build better models faster. Synthesis AI's products include Synthesis Humans, which allows users to create detailed images and videos of digital humans with rich annotations; Synthesis Scenarios, which enables users to craft complex multi-human simulations across a variety of environments; and a range of applications for industries such as ID verification, automotive, avatar creation, virtual fashion, AI fitness, teleconferencing, visual effects, and security.

SQLAI.ai is a professional SQL multi-tool that leverages AI technology to generate, fix, explain, and optimize SQL queries and databases. It enables users to interact with SQL using everyday language, effortlessly train AI to understand database schemas, and benefit from AI-driven recommendations for query optimization. The platform caters to a wide range of users, from beginners to experts, by simplifying SQL tasks and providing valuable insights for database management. With features like generating SQL data, data analytics, and real-time data insights, SQLAI.ai revolutionizes the way users interact with databases, making SQL tasks simpler, more efficient, and accessible to all.

SQLAI.ai is a powerful SQL multi-tool that utilizes AI to generate, fix, explain, and optimize SQL queries and databases. It empowers users to effortlessly create complex SQL queries using everyday language, optimize queries for better performance, fix syntax errors with ease, and gain a deeper understanding of SQL queries through AI-powered explanations. Additionally, SQLAI.ai enables users to train AI on their database schema, ensuring unparalleled accuracy in AI-generated queries and optimizations.

Bodt.io is a no-code AI conversational platform that allows users to create custom chatbots for websites and various messaging platforms. The platform offers features such as easy API integration, custom chatbots trained on user data, multi-platform compatibility, quick deployment, and personalized chatbot creation. Bodt.io provides advantages like lead generation, seamless transition to human support, global reach with multilingual capabilities, reduced customer support costs, and accurate, human-like responses. However, some disadvantages include the need for training the chatbot with user data, potential limitations in handling complex queries, and the requirement for ongoing maintenance and updates.

AiVANTA is a scalable AI SaaS solution that offers Integrated Content Automation and Delivery (ICAD) for enterprises, enabling seamless AI video adoption. The platform provides a single-window solution to simplify all AI video needs, reducing video production costs by 80%. AiVANTA features a rich virtual avatar library, managed services for refining AI output, and tailored AI videos for various business needs. Its advantages include cost reduction, multi-lingual content, customization, quick turnaround, and trusted by brands. However, the disadvantages include the need for oversight in managed services, limited customization options, and potential language barriers. The application is suitable for BFSI, Healthcare, E-Commerce, and corporate training. Users can utilize AiVANTA for tasks like creating product information videos, training content, engaging with customers, and producing podcasts and explainer videos.

UBIAI is a powerful text annotation tool that helps businesses accelerate their data labeling process. With UBIAI, businesses can annotate any type of document, including PDFs, images, and text. UBIAI also offers a variety of features to make the annotation process easier and more efficient, such as auto-labeling, multi-lingual annotation, and team collaboration. With UBIAI, businesses can save time and money on their data labeling projects.

Yellow.ai is a leading provider of AI-powered customer service automation solutions. Its Dynamic Automation Platform (DAP) is built on multi-LLM architecture and continuously trains on billions of conversations for scale, speed, and accuracy. Yellow.ai's platform leverages the latest advancements in NLP and generative AI to deliver empathetic and context-aware conversations that exceed customer expectations across channels. With its enterprise-grade security, advanced analytics, and zero-setup bot deployment, Yellow.ai helps businesses transform their customer and employee experiences with AI-powered automation.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It supports training, inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. The framework includes various training technologies and accelerates inference, evaluation, and deployment modules. It offers a Gradio-based Web-UI interface and best practices for easy application of large models. ms-swift supports a wide range of model types, dataset types, hardware support, lightweight training methods, distributed training techniques, quantization training, RLHF training, multi-modal training, interface training, plugin and extension support, inference acceleration engines, model evaluation, and model quantization.

Recent LLM (Large Language Models)-based CV and multi-modal works.

This repository contains the source code for the SIGIR 2023 paper 'Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited'. It provides resources for evaluating foundation, transferable, multi-modal, and LLM recommendation models, along with datasets, pre-trained models, and training strategies for IDRec and MoRec using in-batch debiased cross-entropy loss. The repository also offers large-scale datasets, code for SASRec with in-batch debias cross-entropy loss, and information on joining the lab for research opportunities.

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.

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.

Grounded 3D-LLM is a unified generative framework that utilizes referent tokens to reference 3D scenes, enabling the handling of sequences that interleave 3D and textual data. It transforms 3D vision tasks into language formats through task-specific prompts, curating grounded language datasets and employing Contrastive Language-Scene Pre-training (CLASP) to bridge the gap between 3D vision and language models. The model covers tasks like 3D visual question answering, dense captioning, object detection, and language grounding.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

MiniCPM is a series of open-source large models on the client side jointly developed by Face Intelligence and Tsinghua University Natural Language Processing Laboratory. The main language model MiniCPM-2B has only 2.4 billion (2.4B) non-word embedding parameters, with a total of 2.7B parameters. - After SFT, MiniCPM-2B performs similarly to Mistral-7B on public comprehensive evaluation sets (better in Chinese, mathematics, and code capabilities), and outperforms models such as Llama2-13B, MPT-30B, and Falcon-40B overall. - After DPO, MiniCPM-2B also surpasses many representative open-source large models such as Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, and Zephyr-7B-alpha on the current evaluation set MTBench, which is closest to the user experience. - Based on MiniCPM-2B, a multi-modal large model MiniCPM-V 2.0 on the client side is constructed, which achieves the best performance of models below 7B in multiple test benchmarks, and surpasses larger parameter scale models such as Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on the OpenCompass leaderboard. MiniCPM-V 2.0 also demonstrates leading OCR capabilities, approaching Gemini Pro in scene text recognition capabilities. - After Int4 quantization, MiniCPM can be deployed and inferred on mobile phones, with a streaming output speed slightly higher than human speech speed. MiniCPM-V also directly runs through the deployment of multi-modal large models on mobile phones. - A single 1080/2080 can efficiently fine-tune parameters, and a single 3090/4090 can fully fine-tune parameters. A single machine can continuously train MiniCPM, and the secondary development cost is relatively low.

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Formador para la certificación de ML Engineer en GCP, con respuestas y explicaciones detalladas.