hugging
There are 17 repositories under hugging topic.
noahgift/rust-mlops-template
A work in progress to build out solutions in Rust for MLOPs
noahgift/cloud-data-analysis-at-scale
[Course-2020-2023] taught at Duke MIDS. This is also a Coursera Course that covers MLOps, ML Engineering and the foundations of Cloud Computing for Data Science.
Circuit-Overtime/elixpo_ai_chapter
A Collaborative Repository Focusing on Machine Learning and Image-Generation!
kenzic/run-models-in-the-browser-with-transformers.js-demo
Working demo for the article Run Models in the Browser With Transformers.js
PRITHIVSAKTHIUR/Text-to-Image
Text to Image Gen [ Demo ]
mftnakrsu/genAI_application_gradio
Generative AI Applications
Shoaib-33/Fighter-Jet-Image-Recogniser
Link to Git Page
walidboulanouar/Blendify
Blendify the Official Website for the Hackathon GAIA
DarkDk123/AI-Web-Scraper
AI Web Scraper to scrape simple webpages using an LLM.
Erb3/Hugger
Hugging plugin for Minecraft
moreluis/learnlab
🚀 Fullstack AI Flashcard Application ‒ Nuxt.js, TypeScript REST API, MongoDB & Hugging Face ✨
oya163/bert-llm
Usage of Hugging Face library to fine-tune large language models to perform various tasks (QA, NER, SA)
inirah02/EDA-101
Exploratory data analysis techniques using taylor swift's discography data
JustalK/POC-NLP
This project is a POC on Spacy and Hugging Face. I have discovered the NLP recently and I wanted to understand how it works and what we can do with it. So I read the documentation from start to end and made some experience with the library. The project is made with Python.
rn0x/transtexa
A versatile library for text translation using Hugging Face's Transformers
varunajmera0/MetaGenAI
Automate metadata extraction for Parquet & ORC datasets (schema, outliers, contextual, skewness, semanto) with this toolkit. Compatible with Google Gemma and Meta Llama frameworks.
Vasugi2003/AGRO-BRAIN-AI
AGRO BRAIN AI - Crop Disease Prediction Project Overview : AGRO BRAIN AI - is an advanced crop disease prediction system leveraging deep learning techniques to help farmers and agricultural professionals detect and manage crop diseases effectively. By utilizing state-of-the-art models and deploying them on accessible platforms, this project