savanladani/week2-llama3.2-1B

Model Overview

This model is a fine-tuned variant of Llama-3.2-1B, leveraging ORPO (Optimized Regularization for Prompt Optimization) for enhanced performance. It has been fine-tuned using the mlabonne/orpo-dpo-mix-40k dataset as part of the Finetuning Open Source LLMs Course - Week 2 Project.

Intended Use

This model is optimized for general-purpose language tasks, including text parsing, understanding contextual prompts, and enhanced interpretability in natural language processing applications.

Evaluation Results

The model was evaluated on the following benchmarks, with the following performance metrics:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
eq_bench	2.1	none	0	eqbench	↑	1.5355	±	0.9174
		none	0	percent_parseable	↑	16.9591	±	2.8782
hellaswag	1	none	0	acc	↑	0.4812	±	0.0050
		none	0	acc_norm	↑	0.6467	±	0.0048
ifeval	4	none	0	inst_level_loose_acc	↑	0.3993	±	N/A
		none	0	inst_level_strict_acc	↑	0.2974	±	N/A
		none	0	prompt_level_loose_acc	↑	0.2754	±	0.0192
		none	0	prompt_level_strict_acc	↑	0.1848	±	0.0167
tinyMMLU	0	none	0	acc_norm	↑	0.3996	±	N/A

Key Features

• Model Size: 1 Billion parameters
• Fine-tuning Method: ORPO
• Dataset: mlabonne/orpo-dpo-mix-40k