huggingface

Workshop for making a Telegram bot and using APIs.

The project,being part of Kagglex BIPOC Mentorship Program final project, aims to train two separate Hindi ASR models using the Facebook Wav2Vec2 (300M parameters) and OpenAI Whisper-Small models, respectively. The goal is to compare their performance, with a target WER of less than 13%, across various Hindi accents and dialects.

Use Transformers.js from Blazor WebAssembly to run pretrained models with the ONNX Runtime in the browser.

Gradio-based web UI for medical image interpretation using Google's MedGemma vision-language model.

Wrapper for stable version of RoBERTa language models

Easy to use command line assistant using OpenAssistant's model API

Learn LangChain for Genearative AI with OpenAI API using Python

One API to access Large Language Models in Java

Library for Federated Emergence & Foundation Models

Using Semantic Kernel to obtain answer from a PDF document, with embeddings stored in Redis and HuggingFace to create embeddings.

🤗📝 HuggingFace assisted Obsidian plugin

A WebML based Vision-Language App that Roasts you like an Asian Mom

A fast CPU-first video/audio transcriber for generating caption files with Whisper and CTranslate2, hosted on Hugging Face Spaces.

Finetuning Stable Diffusion from Diffusers

Containerized self-hosted REST API for vision classification, utilizing Hugging Face transformers.

End-to-end integration of HuggingFace's models for sequence labeling.

🧠🃏 Your universal model catalog, everything, everywhere, all at once.

Transformers PHP is a toolkit for PHP developers to add machine learning magic to their projects easily.

Engineered an advanced deep learning model to automate the classification of financial documents, including Balance Sheets, Cash Flow and Income Statements using Bidirectional LSTM and TensorFlow. The model achieved an impressive accuracy of 96.2%, enhancing efficiency and reducing errors in document management for the finance and banking sectors.

Convert pretrained RoBerta models to various long-document transformer models