docs: Updated readme

README.md

@@ -18,4 +18,49 @@ hf_oauth_scopes:
 - inference-api
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+<div class="header-container">
+    <div class="logo-container">
+        <a href="https://github.com/argilla-io/distilabel" target="_blank" rel="noopener noreferrer">
+            <img src="https://distilabel.argilla.io/latest/assets/distilabel-black.svg" alt="Distilabel Logo" style="width: 150px; height: auto;">
+        </a>
+    </div>
+    <div class="title-container">
+        <h1 style="margin: 0; font-size: 2em;">🧬 Synthetic Data Generator</h1>
+        <p style="margin: 10px 0 0 0; color: #666; font-size: 1.1em;">Build datasets using natural language</p>
+    </div>
+</div>
+<br>
+This repository contains the code for the [free Synthetic Data Generator app](https://huggingface.co/spaces/argilla/synthetic-data-generator), which is hosted on the Hugging Face Hub.
+
+## How it works?
+
+![Synthetic Data Generator](https://huggingface.co/spaces/argilla/synthetic-data-generator/resolve/main/assets/flow.png)
+
+Distilabel Synthetic Data Generator is an experimental tool that allows you to easily create high-quality datasets for training and fine-tuning language models. It leverages the power of distilabel and advanced language models to generate synthetic data tailored to your specific needs.
+
+This tool simplifies the process of creating custom datasets, enabling you to:
+
+- Define the characteristics of your desired application
+- Generate system prompts and tasks automatically
+- Create sample datasets for quick iteration
+- Produce full-scale datasets with customizable parameters
+- Push your generated datasets directly to the Hugging Face Hub
+
+By using Distilabel Synthetic Data Generator, you can rapidly prototype and create datasets for, accelerating your AI development process.
+
+## Do you want to run this locally?
+
+You can simply clone the repository and run it locally with:
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Do you need more control?
+
+Each pipeline is based on a distilabel component, so you can easily run it locally or with other LLMs.
+
+Check out the [distilabel library](https://github.com/argilla-io/distilabel) for more information.