🔥Your Daily Dose of AI Research from Hugging Face 🔥 Stay updated with the latest AI breakthroughs! This bot automatically collects and analyzes papers from 🤗 Hugging Face's daily papers, providing Chinese translations and AI-powered interpretations. Powered by state-of-the-art language models, it brings cutting-edge research to your finger

MCP server for OpenAI's Deep Research APIs, Gemini Deep Research Agent, and Hugging Face's Open Deep Research

The course introduces the use of open-source large language models (LLMs) from the Hugging Face ecosystem for research in the behavioral and social sciences.

SPIDER: A multi-organ pathology dataset with expert annotations and pre-trained models for AI-driven research. Available on Hugging Face 🤗.

The course introduces the use of open-source large language models (LLMs) from the Hugging Face ecosystem for research in the behavioral and social sciences.

🤖 A multilingual translation tool that automatically converts Hugging Face's daily AI research papers into 🇯🇵 Japanese, 🇰🇷 Korean, 🇪🇸 Spanish, and 🇫🇷 French. 🔄 Powered by InternLM API, it accurately translates paper titles and abstracts while maintaining technical accuracy. 🛠️ The system features robust error handling, progress tracking,

This course introduces the use of open-source large language models (LLMs) from the Hugging Face ecosystem for research in the behavioral and social sciences.

How to Distill an LLM? LLM Step by Step Distillation [Research Paper] Implementation using Python and Hugging Face Auto Train

The Document Summarizer leverages Hugging Face’s facebook/bart-large-cnn model to transform lengthy documents into concise summaries. Built with ReactJS (Vite) for the frontend and Flask for the backend, it supports PDF and text files, offering real-time summarization for researchers, students, and professionals.

Synthia is an AI Discord bot that blends research news from Hugging Face, arXiv, and MarkTechPost with humanlike warmth. She welcomes new members with humor and keeps the server up to date with the latest in AI and tech — a smart and social sidekick for your community.

TinyLLM is a research project focused on developing and training compact, specialized language models using publicly available datasets from platforms like Hugging Face. The project aims to explore diverse architectures and build expertise toward creating a high-performance coding model rivaling the capabilities of claude code

A self-taught AI/ML Engineer and Data Scientist showcase of 30+ production-ready and research projects in Machine Learning, Deep Learning, NLP, and MLOps. Built end-to-end pipelines, real-time systems, and deployable apps using tools like Docker, Kubernetes, Airflow, MLflow, TensorFlow, PyTorch, Hugging Face, and cloud platforms (GCP, AWS).

Gridcoin Research HUG REST API

CLI for Hugging Face Papers API — search, read, and explore AI research papers from the terminal

个人转化的讯飞星火 Spark-13B (2024版) 的 Hugging Face 实现。供社区研究早期大模型架构演进与学习用途。A Hugging Face compatible implementation of the early 2024 iFlytek Spark 13B Chat model. Converted for educational and research purposes to study LLM architecture evolution.

A tutorial for using Hugging Face transformers on Princeton University research computing clusters.

An end‑to‑end multi‑agent pipeline that turns a simple topic into a 30‑second video by researching the niche, writing a 6‑segment script, generating AI voiceover and images (OpenAI, Hugging Face, or Pollinations), animating clips with MoviePy, and assembling the final MP4.

The Keyword Extractor leverages Hugging Face’s facebook/bart-large-mnli model to extract relevant keywords or categories from text. Built with a ReactJS (Vite) frontend and a Flask backend, this tool delivers real-time, AI-powered keyword extraction. Perfect for content creators, researchers, and marketers!

Mithridatium is a research-driven project aimed at detecting backdoors and data poisoning in downloaded pretrained models or pipelines (e.g., from Hugging Face). Our goal is to provide a modular, command-line tool that helps researchers and engineers trust the models they use.

AI Research Summarizer Tool built with LangChain, Hugging Face, and Streamlit.

This repository hosts a RAG (Retrieval-Augmented Generation) application using Langchain, enabling users to query over 100 research papers on GANs. It provides accurate, context-based answers using the Gemma2-9b-It LLM model, powered by the Groq API and Hugging Face embeddings, making it an essential tool for GAN researchers.

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RAG based pipeline for automated research paper summarization using Llamaindex, Mistral AI, and Hugging Face embeddings. This tool parses and summarizes academic documents with customizable chunking and asynchronous tree-based summarization.

A friendly companion extension that "hugs" and simplifies complex research papers for better understanding.

A multi-agent system leveraging Hugging Face's smolagents to automate content creation by coordinating research, writing, and editing tasks through collaborative AI agents.

An automated AI research assistant that scrapes Hugging Face Papers daily, filters them by your interests using Google Gemini, and delivers deep analytical summaries to Discord threads.

An integrated suite of AI-powered Streamlit apps built to assist legal professionals, clients, and researchers with document analysis, legal drafting, and legal clarifications. Powered by Google Gemini LLM, Hugging Face Transformers, and modern UI/UX practices.

RAG Research Paper Q&A is a generative AI application built with Streamlit that provides users with a question-and-answer interface for research papers. The application leverages Groq's powerful language model and Hugging Face embedding to deliver contextually relevant response based on user queries. Users interact with documents stored in a folder

Khulasah is an Arabic PDF summarizer that extracts and condenses text using the ALLaM-7B-Instruct model via Hugging Face API. It features smart preprocessing and delivers high-quality Arabic summaries—ideal for developers, researchers, and journalists. No GPU or downloads needed.

AI-powered, fully local GenAI app that helps you read, summarize, question, and quiz yourself on research papers or text documents — completely offline, no paid API key required. Built with Python, Hugging Face Transformers, Faiss & Gradio to keep your data private and your workflow smarter.