advances in ai 2024

International Summer School in Advances in AI

Large Language Models and How to Instruction Tune Them (in a Sustainable Way)

Authors: Danilo Croce

Many thanks to: Claudiu Daniel Hromei for supporting the development of (most of the) code

This repository hosts materials from the Lecture held in the Summer School Andvances in AI 2024 organized by the AIxIA.

The objective of this lecture is:

• Introduce Transformer-based architectures, including encoding-decoding, encoder-only, and decoder-only structures.
• Demonstrate fine-tuning of Large Language Models (LLMs) on diverse datasets in a multi-task framework.
• Utilize Low-Rank Adaptation (LoRA) for sustainable and efficient tuning on "modest" hardware (e.g., single 16GB RAM GPU).

The repository includes code for fine-tuning a Large Language Model (based on LLaMA) to solve NLP tasks, such as the ones proposed in EVALITA 2023.

Code

Lab : Fine-tune a LLaMA-based model for all tasks from EVALITA 2023

At the end, this lecture shows how to encode data from different tasks into specific prompts and fine-tune the LLM using Q-LoRA. The code can be also used in Google Colab using an Nvidia-T4 GPU with 15GB memory.

The code is heavily based on the one used in ExtremITA system participating to EVALITA 2023:

• ExtremITA Paper
• ExtremITA Github Code

The overall process is divided in four steps:

• Step 1 - Encoding the data: it shows how to encode data from an EVALITA task to generate prompts for the LLM
• Step 2 - Fine-tuning the LLaMA model: it shows how to fine-tune the LLMS given the prompts
• Step 3 - Inference: generating answers: it shows how to use the fined-tuned model
• Step 4 - Deconding the data: it shows how to convert the data to be evaluated in the EVALTA challenge

Slides

The repository also features slides (LINK).

Excercise

An excercise is proposed in the last slides of the presentation.

Contacts

For queries or suggestions, raise an Issue in this repository or email [email protected]