A framework for large scale recommendation algorithms.

A compilation of the best multi-agent papers

[NeurIPS 2023] Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation

InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.

Repository for Project Insight: NLP as a Service

Sparse Inferencing for transformer based LLMs

Federated Learning Utilities and Tools for Experimentation

CLIP (Contrastive Language–Image Pre-training) for Italian

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

A comprehensive web application that detects AI-generated content in PDF documents and transforms AI text into natural human-like writing. Built with Streamlit, spaCy, and Hugging Face transformers.

Retrieval-based Voice Conversion (RVC) implemented with Hugging Face Transformers.

Pytorch implementation of image captioning using transformer-based model.

I will implement Fastai in each projects present in this repository.

This project investigates the security of large language models by performing binary classification of a set of input prompts to discover malicious prompts. Several approaches have been analyzed using classical ML algorithms, a trained LLM model, and a fine-tuned LLM model.

Official repository for the paper "ALERT: A Comprehensive Benchmark for Assessing Large Language Models’ Safety through Red Teaming"

[TMI 2023] XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

This repository contain my 75Day Hard Generative AI and LLM Learning Challenge.

A radically simple, reliable, and high performance template to enable you to quickly get set up building multi-agent applications

An ASR (Automatic Speech Recognition) adversarial attack repository.

An open source community implementation of the model from "DIFFERENTIAL TRANSFORMER" paper by Microsoft.