Smanj/Fina_l1

Final Project

Project Overview

This project involves fine-tuning or training a pretrained Large Language Model (LLM) to follow instructions in assigned languages. The objective is to enable the model to generate outputs in the same language as the input query.

Project Objectives

1. Fine-tune an LLM to handle instructions in the assigned languages.
2. Evaluate the LLM’s performance using multilingual input and output scenarios.

Deliverables

1. Notebooks Training Notebook: Includes the fine-tuning process, dataset preparation, and hyperparameter details. Evaluation Notebook: Details the evaluation strategy, performance metrics, and multilingual outputs.
2. Documentation Model Selection: Describe the primary and secondary models chosen and the criteria for selection. Hyperparameters: Document and explain hyperparameters used, including the ones that worked and failed. Evaluation Strategy: Justify the evaluation methodology chosen.
3. Results Present outputs in assigned languages. Include screenshots of Google Translate or equivalent tools to verify correctness.

How to Use Clone the repository: git clone cd Install the required packages: Ensure you have the necessary libraries installed. Can be done by running:

pip install -r requirements.txt Run the Jupyter Notebook: Open the notebook in Jupyter:

jupyter notebook Training_Finnish.ipynb and Evaluation_Finnish.ipynb