JamieAi33/Phi-2_PEFT

license: apache-2.0 language: - en metrics: - rouge base_model: - microsoft/phi-2 pipeline_tag: question-answering

Model Card for PEFT-Fine-Tuned Model

This model card documents a PEFT-fine-tuned version of microsoft/phi-2 for question-answering tasks. The PEFT fine-tuning improved the model's performance, as detailed in the evaluation section.

Model Details

Model Description

• Developed by: User (you can replace with your name or organization)
• Finetuned from model: microsoft/phi-2
• Model type: PEFT fine-tuned transformer
• Language(s) (NLP): English
• License: Apache 2.0

The base model microsoft/phi-2 was adapted using Parameter-Efficient Fine-Tuning (PEFT) for question-answering tasks. The training process focused on improving performance metrics while keeping computational costs low.

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Model Sources

• Repository: https://huggingface.co/JamieAi33/Phi-2_PEFT

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Uses

Direct Use

This model can be used out-of-the-box for question-answering tasks.

Downstream Use

The model can be fine-tuned further on domain-specific datasets for improved performance.

Out-of-Scope Use

Avoid using this model for tasks outside question-answering or where fairness, bias, and ethical considerations are critical without further validation.

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Bias, Risks, and Limitations

Users should be aware that:

• The model is trained on publicly available data and may inherit biases present in the training data.
• It is optimized for English and may perform poorly in other languages.

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How to Get Started with the Model

Here’s an example of loading the model:

from transformers import AutoModel
from peft import PeftModel

base_model = AutoModel.from_pretrained("microsoft/phi-2")
adapter_model = PeftModel.from_pretrained(base_model, "Phi-2_PEFT")




# Model Name: PEFT Fine-Tuned `microsoft/phi-2`

This repository contains a PEFT fine-tuned version of the `microsoft/phi-2` model for question-answering tasks. The fine-tuning process leveraged Parameter-Efficient Fine-Tuning (PEFT) techniques to achieve improved performance.

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## Metrics

The model's performance was evaluated using the ROUGE metric. Below are the results:

| **Metric**     | **Original Model** | **PEFT Model** | **Absolute Improvement** |
|-----------------|--------------------|----------------|---------------------------|
| **ROUGE-1**    | 29.76%             | 44.51%         | +14.75%                  |
| **ROUGE-2**    | 10.76%             | 15.68%         | +4.92%                   |
| **ROUGE-L**    | 21.69%             | 30.95%         | +9.25%                   |
| **ROUGE-Lsum** | 22.75%             | 31.49%         | +8.74%                   |

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