My lab work of “Generative AI with Large Language Models” course offered by DeepLearning.AI and Amazon Web Services on coursera.

practical projects using LLM, VLM and Diffusion models

NTU Deep Learning for Computer Vision 2023 course

Fine Tuning pegasus and flan-t5 pre-trained language model on dialogsum datasets for conversation summarization to to optimize context window in RAG-LLMs

This repo contains implementations of fine-tuning LLaMA LLM model using LoRA weights (PEFT) as well as focuses on the Retrieval Augmented Generation (RAG) framework.

The transformation of education starts here! Where AI and educational robotics come together to provide the best learning experience.

A fine-tuned LLM great at answering questions about car repairs and maintenance.

Finetuning Large Language Models

In this repo I will share different topics on anything I want to know in nlp and llms

This repository was commited under the action of executing important tasks on which modern Generative AI concepts are laid on. In particular, we focussed on three coding actions of Large Language Models. Extra and necessary details are given in the README.md file.

Practical work of my master's thesis

This repository contains a collection of generative AI models and applications designed for various tasks such as text generation, image synthesis, and style transfer. The models leverage cutting-edge architectures like GPT, GANs, and VAEs, enabling users to explore different generative tasks.

The task of this project is to Convert Natural Language to SQL Queries

Stumble upon a fine tuning that is unfathomable.

PEFT and LoRA to fine-tune large language models for dialogue summarization, reducing computational resources for broader application.

Fine-tuning an LLM to generate musical micro-genres

Unlocking the Power of Generative AI: In-Context Learning, Instruction Fine-Tuning and Reinforcement Learning Fine-Tuning.

Dialogue Summary LLM - FLAN - T5: An implementation of the Flan-t5 LLM to summarize dialogues. Prompt Engineering , Fine tuning with PEFT and fine tuning with RL (PPO) is explored within this project.