Our research project for NLP class in University of Ljubljana that I'm one of the contributors.

My personal notes, code and projects of the Udacity Generative AI Nanodegree.

This project demonstrates how to fine-tune a large language model (LLM) for the IT domain using Amazon SageMaker, creating an "IT Domain Expert" model.

LLM Model: Fine-tuning, Evaluation, Containerization, Deployment, CI/CD Pipeline

A winner of NeurIPS LLM 2023 Competition

Fine-tune large language models (LLMs) using the Hugging Face Transformers library.

Gemma-2b-it LLM has been finetuned on a dataset of Python codes, enabling it to proficiently learn Python syntax and assist in debugging tasks, offering valuable guidance to programmers.

A collection of examples for training or fine-tuning LLMs.

Factuality check of the SemRep Predications

nter the realm of truth detection with GPT-Truth - fine-tuning GPT-3.5 for unparalleled accuracy in identifying deceptive opinions

simplifies fine-tuning and inference for 60+ open-source LLMs through a single API

The project was undertaken as part of the Intel Unnati Industrial Training program for the year 2024. The primary objective of this project aligns with Problem Statement PS-04: Introduction to GenAI LLM Inference on CPUs and subsequent LLM Model Finetuning for the development of a Custom Chatbot.

Comparison of different adaptation methods on PEFT for fine-tuning downstream tasks or benchmarks.

Fine-tuning BERT on the SQuAD dataset for Question-Answering tasks

A payload compression toolkit that makes it easy to create ideal data structures for LLMs; from training data to chain payloads.

Natural Language Processing Class Project - Spring '23. Analysing and Generating Sports Fans Responses from Reddit Sport Subreddits

This is a package for generating questions and answers from unstructured data to be used for NLP tasks.

Streamlit application for Reddit posts powered by OpenAI, Pinecone and Langchain

A helper library for fine-tuning Amazon Bedrock models. This toolkit assists in generating Q&A datasets from documents and streamlines the LLM fine-tuning process.