---
library_name: Transformers
tags:
- transformers
- fine-tuned
- language-modeling
- direct-preference-optimization
base_model: RatanRohith/NeuralPizza-7B-V0.1
datasets:
- Intel/orca_dpo_pairs
license: apache-2.0
---

## Model Description

NeuralPizza-7B-V0.3 is a fine-tuned version of the RatanRohith/NeuralPizza-7B-V0.1 model, specialized through Direct Preference Optimization (DPO). It was fine-tuned using the argilla/distilabel-intel-orca-dpo-pairs dataset, focusing on enhancing model performance based on preference comparisons.

## Intended Use

This model is primarily intended for research and experimental applications in language modeling, especially for exploring the Direct Preference Optimization method. It provides insights into the nuances of DPO in the context of language model tuning.

## Training Data

The model was fine-tuned using the argilla/distilabel-intel-orca-dpo-pairs dataset. This dataset is designed for applying and testing Direct Preference Optimization techniques in language models.

## Training Procedure

The training followed the guidelines and methodologies outlined in the "Fine-Tune a Mistral 7B Model with Direct Preference Optimization" guide from Medium's Towards Data Science platform. Specific training regimes and hyperparameters are based on this guide. Here : https://medium.com/towards-data-science/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac

## Limitations and Bias

As an experimental model, it may carry biases inherent from its training data. The model's performance and outputs should be critically evaluated, especially in sensitive and diverse applications.