Research & Education

FlUX.1 LoRA is an AI image generation tool available as a Hugging Face Space. Users can input a text prompt and an optional negative prompt to describe their desired image. The tool provides options to adjust image size and sampling steps, and allows selection of different FLUX LoRA adapters to influence the generated output. While the space is currently paused, it is designed to create high-quality images based on user specifications. The tool is built on the Gradio framework and is licensed under MIT, indicating its open-source nature and potential for community contributions.

FLUX.1 RealismLora is an AI image generation tool built as a Hugging Face Space, allowing users to create high-quality, realistic images from text prompts. It leverages the Flux model and is enhanced with LoRA technology to produce detailed and lifelike visuals. Users can customize various parameters, including image size, the number of generation steps, and the strength of the LoRA application, to fine-tune the output to their specific needs. This tool is ideal for anyone looking to generate realistic imagery for creative projects, content creation, or educational purposes, offering a user-friendly interface for prompt-based image generation.

Sales Caddy AI is an innovative platform designed to revolutionize sales coaching through personalized, AI-driven insights. It combines decades of experience from sales leadership and elite athletic coaching to help sales professionals achieve a winning mindset and optimize their performance. The tool offers 1-2-1 personalized coaching tailored to individual strengths and goals, leveraging expert-led sessions. The Sales Caddy AI app provides real-time, AI-powered guidance to stay competitive and close more deals faster. Additionally, it includes interactive learning modules, industry insights, and proven sales strategies for continuous skill development. Sales Caddy AI aims to empower both new and seasoned sales professionals to boost conversion rates, enhance productivity, and develop the confidence needed to outperform competitors.

Gligen Demo is a platform designed to showcase the capabilities of the Gligen model for image generation. It provides a public interface for users to interact with and test the model's functionalities. While the current live website indicates a runtime error, suggesting temporary unavailability, the tool's purpose is to allow exploration of advanced image generation techniques. It is particularly suitable for AI researchers, developers, and creative professionals who are interested in understanding and experimenting with cutting-edge AI models in the field of visual content creation. The demo aims to provide insights into how the Gligen model can be utilized for various image generation tasks.

GOT OCR Transformers is a demonstration of the GOT-OCR 2.0's Transformers implementation, hosted on Hugging Face. This application enables users to perform Optical Character Recognition (OCR) by uploading an image and selecting their preferred OCR method. It is designed for extracting text from various image formats, providing a straightforward interface for text recognition tasks. While the current live website indicates a runtime error, the tool's core functionality is centered around advanced OCR capabilities, making it useful for researchers and developers in the field of text extraction and document processing.

GLM 4.5 Demo (API) offers a direct interface to the GLM-4.5 model, enabling users to engage in conversational AI interactions. This tool is designed to provide comprehensive answers and logical reasoning to a wide range of queries. Users can input text messages and receive responses that can optionally include step-by-step thinking processes, making it valuable for understanding how the AI arrives at its conclusions. Hosted on Hugging Face Spaces, it serves as an accessible platform for developers, researchers, and AI enthusiasts to experiment with and integrate advanced conversational AI capabilities into their projects or explore its potential for various applications.

GLM OCR Demo is a multimodal OCR model designed for complex document understanding, available as a Hugging Face Space. This application allows users to upload an image and specify whether they want to extract plain text, mathematical formulas, or table data. After processing, the recognized content is returned in an editable format. This tool is particularly useful for researchers and developers working with OCR technology who need to analyze intricate documents, offering a flexible solution for various data extraction needs from visual inputs.

LongCite is an open-source project designed to enhance Large Language Models (LLMs) by enabling them to generate fine-grained, sentence-level citations within long-context question answering scenarios. This capability significantly improves the verifiability and accuracy of LLM outputs. The project offers two pre-trained models, LongCite-glm4-9b and LongCite-llama3.1-8b, which support up to 128K context and are based on GLM-4-9B and Meta-Llama-3.1-8B, respectively. LongCite also provides a CoF (Coarse to Fine) pipeline for automated SFT data construction, allowing users to generate high-quality long-context QA instances with fine-grained citations. Additionally, it includes an automatic benchmark, LongBench-Cite, for evaluating citation quality and response correctness.

Genius is an AI tool hosted on Hugging Face Spaces, developed by beyond. While the current live website indicates a runtime error, the tool's JSON-LD schema identifies it as a WebApplication with an 'AIApplication' category. The error message suggests it is intended for 'text2text-generation' using a model named 'beyond/genius-base-chinese'. It is licensed under Apache-2.0, indicating it is likely an open-source or freely available project for developers and researchers interested in text generation tasks, particularly in Chinese.

LLM_MultiAgents_Survey_Papers is a GitHub repository dedicated to surveying the rapidly evolving field of Large Language Model (LLM) based Multi-Agents. It provides a curated collection of research papers, offering an overview of current progress and identifying key challenges. The repository categorizes these papers into five main streams: Multi-Agents Framework, Multi-Agents Orchestration and Efficiency, Multi-Agents for Problem Solving, Multi-Agents for World Simulation, and Multi-Agents Datasets and Benchmarks. This structured approach helps researchers and academics navigate the extensive literature. The project also includes a summarized LLM-based Multi-Agents architecture and an overview table, making it a valuable resource for understanding the landscape of multi-agent systems powered by large language models.

DAIMLAS is a leading platform designed to build, manage, and grow national and local artificial intelligence ecosystems. It serves governments by helping implement national AI strategies, enterprises by building internal AI ecosystems and orchestrating AI teams, and academia by connecting them to global AI opportunities, jobs, and funding. The platform focuses on creating inclusive pathways into AI for all stakeholders, including upstream resources, downstream channels, and subsidiary resource providers. DAIMLAS facilitates the creation of AI Centers of Excellence and brings together the 8 key AI stakeholders in a local ecosystem to platformize strategies and achieve AI goals. It also addresses common challenges faced by AI talent, such as gaining real-world experience and working on complex AI projects in teams.

StudyBoosterAI is an AI-powered study companion designed to revolutionize education for IB, IGCSE, ICSE, Diploma, and college students. It generates personalized support materials daily based on classroom learning, sifting through trusted sources to save time. The platform incorporates engaging content with stories, questions, and mnemonic techniques like the 'Memory Palace' to enhance understanding and retention. StudyBoosterAI also helps create personalized study plans and breaks down resources into bite-sized, 10-minute learning actions, fostering consistent study habits. It leverages globally recognized learning frameworks such as Bloom's Taxonomy, Narrative Pedagogy, Mnemonics, and Atomic Habits to make learning effective and enjoyable.

MARL-Papers offers a meticulously curated collection of research papers focused on multi-agent reinforcement learning (MARL). This resource is invaluable for researchers and practitioners, providing an organized overview of the field's foundations, modern approaches, and applications. The collection includes tutorials, review papers, and research papers, all sorted chronologically for easy navigation. It covers various sub-topics within MARL, such as joint action learning, cooperation and competition, coordination, security, self-play, learning to communicate, transfer learning, imitation, inverse reinforcement learning, meta-learning, and applications in Large Language Models (LLMs) and robotics. The platform encourages community contributions, making it a dynamic and up-to-date repository for MARL literature.

GLM 130B is an AI tool that provides access to the GLM-130B large language model for research and experimentation. Users can interact with the model by typing prompts in either English or Chinese. The tool supports fill-in-the-blank tasks using `[MASK]` and continuation tasks using `[gMASK]`. This platform is ideal for researchers and developers looking to explore the capabilities of a large-scale AI model. It offers a straightforward interface for generating text and understanding model behavior, making it a valuable resource for those working in natural language processing and AI development.

GODEL Demo is an AI chatbot demonstration hosted on Hugging Face Spaces by Microsoft. This tool offers users an opportunity to interact directly with a conversational AI model, allowing them to test its capabilities and observe its responses in real-time. While the current status indicates a build error, the intention is to provide a platform for exploring and experimenting with advanced AI models. It serves as a valuable resource for those interested in understanding the practical application and performance of AI in conversational contexts, offering a hands-on experience with cutting-edge technology.

GenAI-Bench Dataset Viewer is a Hugging Face Space designed for exploring and analyzing the GenAI-Bench dataset. Users can browse and filter a vast collection of images based on both basic and advanced skills, providing a comprehensive view of the dataset's contents. The tool facilitates the comparison of images generated by various AI models and includes human ratings for deeper analysis. This interactive viewer is particularly useful for researchers and developers working on generative AI models, offering a visual and interactive way to understand model performance and data characteristics within the GenAI-Bench framework.

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) is dedicated to guiding and developing AI technologies that prioritize human well-being and societal benefit. HAI brings together diverse expertise from fields such as business, economics, law, and medicine to foster interdisciplinary research and collaboration. The institute aims to serve as a global hub for leading AI thinkers, researchers, and policymakers, facilitating discussions and advancements in the ethical and practical applications of AI. Through its initiatives, HAI contributes to the understanding and responsible implementation of AI across various sectors, promoting discovery and learning in this rapidly evolving field.

GHOST 2.0 is an application hosted on Hugging Face Spaces, designed for straightforward face swapping. Users can easily upload a source image containing the face they wish to use and a target image where they want that face to appear. The tool then processes these images to perform the face swap. While the current status indicates a build error, the core functionality described is focused on providing an accessible way to modify images by replacing faces. This tool is developed by ai-forever and is intended for web-based use, making it readily available for anyone interested in image manipulation.

Ginigen Private AI is a specialized AI tool hosted on Hugging Face, designed for private AI interactions. It leverages advanced language models, specifically mentioning Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503, to generate detailed and reasoned responses based on user input. The application emphasizes data privacy, making it suitable for users who prioritize the confidentiality of their interactions. While the current status indicates the Space is paused, its core functionality involves providing a private environment for conversational AI, allowing users to customize and receive comprehensive replies without compromising their data. It is built with Gradio, suggesting an accessible web-based interface.

The GGUF VRAM Calculator is a utility tool hosted on Hugging Face Spaces, designed to assist users in understanding and optimizing VRAM usage for GGUF (GGML Unified Format) AI models. While the live application currently shows a runtime error, its intended purpose is to provide calculations that help users manage their GPU memory efficiently. This is crucial for AI research and development, allowing for better resource allocation and performance tuning of large language models and other AI applications. The tool aims to simplify the complex process of estimating VRAM requirements, which is essential for deploying and running AI models effectively on various hardware configurations.

MedLLMsPracticalGuide is an open-source GitHub repository offering a meticulously curated collection of resources for Medical Large Language Models (Medical LLMs). It serves as a practical guide based on a survey paper published in Nature Reviews Bioengineering, detailing the application of LLMs in medicine. The repository provides an actively updated list of resources, including a Medical LLMs Tree, tables, and papers, covering aspects like building pipelines (pre-training, fine-tuning, prompting), medical data (clinical knowledge bases, pre-training, fine-tuning data), downstream biomedical tasks, clinical applications, challenges, and future directions. It's an invaluable resource for researchers and developers in the medical AI field seeking to understand and implement LLMs.

Gemma 3n E4B It is an AI chatbot designed for multimodal interaction, allowing users to converse with an AI that processes and responds to diverse inputs. This tool can interpret and generate responses based on text, images, sound recordings, and short videos. Users simply type a question or upload relevant files, and the assistant provides helpful replies. It's built as a Hugging Face Space, making it accessible for general AI explorations and interactive demonstrations of multimodal AI capabilities. The tool offers a versatile platform for engaging with AI across different media types.

Grpo Vlm Decoder is a VLM-based message decoder, specifically trained using the GRPO (Gradient-based Reinforcement Learning for Policy Optimization) method. Hosted on Hugging Face Spaces, this tool is freely accessible and built with Gradio, making it suitable for various applications in natural language processing. While the live website currently shows a build error, its intended purpose is to provide a platform for research, development, and educational exploration of VLM decoding techniques. It offers a practical example of applying advanced machine learning models to message interpretation tasks.

gryannote is an AI-powered tool designed for efficient speaker diarization and annotation of audio. Users can easily upload existing audio files or record new audio directly within the platform. The tool automatically processes the audio to identify and label different speakers, streamlining the annotation process. For accuracy, gryannote allows users to manually edit and refine the generated annotations. Once satisfied, the annotated data can be downloaded in the RTTM format, which is widely used for speaker diarization tasks. This makes gryannote particularly useful for researchers, developers, and educators working with audio data and requiring precise speaker identification.