README.md

---
license: mit
---
# Fine-tuned Model for Prompt Enhancement ✍️ 

## Overview

This model is fine-tuned using the QLORA (Quantized Low Rank Adaptation) approach, specifically designed to enhance textual prompts. The primary objective of this model is to take a user's initial prompt and refine it into the best possible version, optimizing clarity, engagement, and effectiveness. This capability makes it an invaluable tool for a wide range of applications, from improving chatbot interactions to enhancing creative writing processes.

## Features

- **Prompt Optimization**: Inputs an initial, potentially unrefined prompt and outputs a significantly improved version.
- **Broad Application**: Suitable for content creators, marketers, developers creating interactive AI, and more.


## How It Works

The model operates by analyzing the structure, content, and intent behind the input prompt. Using the QLORA fine-tuning methodology, it identifies areas for enhancement and generates a revised version that better captures the intended message or question, ensuring higher engagement and clearer communication.

```
Model input: "C program to add two numbers"
Improved Prompt as output:  "Implement a C program that takes two integer inputs and calculates their sum"

Model input: "I wanna learn Martial Arts"
Improved Prompt as output: "Explain the steps one would take to learn martial arts, from beginner to advanced levels."

```


## Usage

This model can be accessed via the Hugging Face API or directly integrated into applications through our provided endpoints. Here's a simple example of how to use the model via the Hugging Face API:

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/zamal/gemma-7b-finetuned"
headers = {"Authorization": "Bearer <your-api-key>"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "Your initial prompt here",
})

print(output)
```