Gemma-7B-Finetuned Open-Source Prompt Optimization Model - Enhance Clarity and Effectiveness of Text Prompts

Gemma 7b Finetuned

Developed by zamal

A prompt optimization model fine-tuned using the QLORA method, specifically designed to enhance the clarity and effectiveness of text prompts.

Large Language Model

Transformers

Open Source License:MIT #Prompt Refinement #QLORA Fine-tuning #AI Interaction Optimization

Downloads 52

Release Time : 3/6/2024

Model Overview

This model analyzes the structure, content, and intent of input prompts to generate clearer and more engaging optimized versions, suitable for content creation, AI interaction development, and other scenarios.

Model Features

Prompt Optimization

Refines initial prompts into clearer and more effective versions to improve communication quality.

QLORA Fine-tuning

Employs Quantized Low-Rank Adaptation for efficient fine-tuning, balancing performance and resource consumption.

Broad Applicability

Supports prompt optimization for various scenarios, from technical programming to creative writing.

Model Capabilities

Text Prompt Optimization

Natural Language Understanding

Text Rewriting

Use Cases

Content Creation

Blog Post Title Optimization

Transforms vague blog topics into eye-catching titles.

Increases reader click-through rates and engagement.

AI Development

Chatbot Prompt Optimization

Optimizes initial prompts for dialogue systems to obtain more accurate responses.

Enhances AI interaction quality and user experience.

Education

Learning Query Reframing

Converts ambiguous learning needs into structured queries.

Obtains more precise learning resources and guidance.

🚀 Fine-tuned Model for Prompt Enhancement ✍️

This fine-tuned model uses the QLORA (Quantized Low Rank Adaptation) approach to enhance textual prompts. It refines a user's initial prompt, optimizing clarity, engagement, and effectiveness. This makes it useful for various applications, such as improving chatbot interactions and creative writing processes.

✨ Features

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

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

This model can be accessed via the Hugging Face API or directly integrated into applications through 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)

Advanced Usage

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."

📚 Documentation

The model is fine-tuned using the QLORA approach to enhance textual prompts. It analyzes the input prompt and generates an improved version.

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the MIT license.

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