Generative AI

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.

Project	Description
LoRA (Low-Rank Adaptation)	LoRA (Low-Rank Adaptation) to fine-tune Foundation Model
GenAI ChatBot	Anmswering Questioning Chatbot
Diffusers	Segmentation and conditioned Inpainting

1 LoRA (Low-Rank Adaptation) to fine-tune Foundation Model

In this project - notebook, I utilized LoRA (Low-Rank Adaptation) to fine-tune DistilGPT2, a foundation model, for a sequence classification task using the SST-2 dataset from the GLUE benchmark. The following steps were performed to implement and adapt the model efficiently:

1.1.Model and Tokenizer Setup:

I started by loading DistilGPT2, a compact variant of GPT-2, using the Hugging Face AutoModelForSequenceClassification class. This base model was configured for a binary classification task with two labels: positive and negative.

I also loaded the corresponding DistilGPT2 tokenizer, ensuring proper tokenization and padding, especially since GPT-2 models typically do not have a padding token by default.

1.2. Dataset: SST-2 from GLUE Benchmark:

The Stanford Sentiment Treebank (SST-2) dataset from the GLUE benchmark was used for training and evaluation. SST-2 is a sentiment classification dataset consisting of movie reviews, where each review is labeled as either positive (1) or negative (0). Given that the dataset exhibited a slight imbalance between the number of positive and negative samples, additional steps were taken to mitigate this imbalance. In essence , I used the F2 score that gives more relevance to false negatives. The next articles were crucial to handle imbalance classes.

https://machinelearningmastery.com/types-of-classification-in-machine-learning/ https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/ https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

1.3 Applying LoRA for Parameter-Efficient Fine-Tuning:

To efficiently fine-tune the model with minimal trainable parameters, I applied LoRA using the PEFT (Parameter-Efficient Fine-Tuning) library. LoRA was specifically applied to the attention layers of the base model, introducing low-rank adaptations that allow the model to be fine-tuned without updating all of its parameters. This reduces the memory and computational requirements compared to traditional fine-tuning.

1.4 Training the LoRA-Adapted Model:

I used Hugging Face’s Trainer API to fine-tune the LoRA-enhanced DistilGPT2 model on the SST-2 dataset. The training loop was configured to evaluate F2 Score at each epoch, and I ensured efficient memory usage by utilizing GPU acceleration when available.

1.5 Evaluation and Saving the Fine-Tuned Model:

After training, I evaluated the model’s performance on the validation set, focusing on F2-score to measure how well the model handled false negatives. Finally, I saved the fine-tuned LoRA model using the PeftModel.save_pretrained() method, making it available for further inference or fine-tuning tasks.

2 Generative AI Chatbot

This repository contains a question-answering chatbot built with generative AI. The chatbot leverages NLP models to provide intelligent, conversational responses, and is implemented as a Jupyter notebook.

For this chatbot project, I chose the nyc_food_scrap_drop_off_sites dataset, which lists food scrap drop-off sites in New York City. This dataset is a good fit because it includes important details like the location, opening hours, and the organization managing each site.

With this information, the chatbot can help users find the nearest drop-off spots, check when they’re open, and see who runs them. Since food scrap drop-off supports waste reduction, this chatbot could also help users learn more about sustainable practices. This dataset allows the chatbot to give clear, useful answers about food scrap disposal in NYC.

Drop-Off Sites in New York City-Maps

2.1 Project Overview

The chatbot in this project demonstrates the use of generative AI for creating natural language responses to user input. It is suitable for various applications, including customer support, information retrieval, and personal assistance.

Features

• Natural Language Processing: Uses transformer-based models to generate conversational responses.
• Interactive Notebook: Implemented in a Jupyter notebook for ease of use and experimentation.
• Customizable: Can be fine-tuned for specific question-answering use cases.

3 Diffusers

This project is part of the GenAI Nanodegree, focusing on creating an interactive application for advanced image editing. Users can select objects within an image to change the background or replace the subject, leveraging cutting-edge AI models.

3.1 Features

Object Selection and Masking:

Use the Segment Anything Model (SAM) to identify and mask objects within an uploaded image. Users can refine masks interactively for precision.

Background Replacement:

Replace the background with a newly generated scene using text-to-image diffusion models. Generate realistic or creative backgrounds based on user-provided descriptions.

Subject Replacement:

Invert the mask to keep the background while swapping out the main subject using text-based prompts.

Acknowledgments Hugging Face for providing transformer models. OpenAI for GPT model architecture inspiration.

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