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:

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)