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README.md

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+# 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.
+
+## 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/<your-username>/<model-name>"
+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)
+
+
+
 ---
 license: mit
 ---