---
tags:
- unsloth
- trl
- sft
---
**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 <repository_url>
cd <repository_directory>
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