Update README.md

README.md

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----
-title: GPT-2 TinyStories Generator
-emoji: 📖
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 5.13.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: A fine-tuned GPT-2 model for generating structured, child-friendly short stories.
----
-
-# GPT-2 TinyStories Generator 📖 FableWeaver AI
-
-## Overview
-This project fine-tunes a **GPT-2 model** on the **TinyStories dataset** to generate structured, coherent, and engaging short narratives. The model is hosted on **Hugging Face Spaces** and provides a user-friendly interface for story generation.
-
-## Features
-- ✅ **Story Generation:** Produces structured, child-friendly short stories.
-- ✅ **Bias Monitoring:** Ensures balanced gender and cultural representation.
-- ✅ **Efficient Training:** Fine-tuned on **200,000 training samples** and **20,000 test samples**.
-- ✅ **Grammar & Readability Enhancements:** Integrated grammar-checking tools and text refinement.
-- ✅ **Optimized Performance:** Uses loss tracking, sampling techniques, and bias mitigation strategies.
-
-## 🚀 System Architecture
-The model is designed for easy interaction via Hugging Face Spaces and follows this workflow:
-1. **Data Preprocessing & Cleaning**
-   - Tokenization, formatting, and encoding normalization.
-   - Bias mitigation and balanced data preparation.
-2. **Model Fine-Tuning**
-   - Fine-tuned **GPT-2 (124M parameters)** using **Hugging Face Transformers**.
-   - Hyperparameter optimization (batch size, learning rate, weight decay).
-3. **Story Generation Pipeline**
-   - Uses **top-k filtering (k=50)**, **top-p nucleus sampling (p=0.9)**, and **temperature adjustments**.
-4. **Post-Processing & Bias Mitigation**
-   - Contextual reinforcement and diversity-aware storytelling.
-   - No-repeat n-gram settings and logical scene transitions.
-5. **Evaluation & Performance Monitoring**
-   - Tracked using **Weights & Biases (W&B)** and **TensorBoard**.
-   - Analyzed validation loss and coherence checks.
-
-## 🔹 Getting Started
-### Accessing the Model
-The model is available on **Hugging Face Spaces**: [GPT-2 TinyStories Generator](https://huggingface.co/spaces/cpv2280/gpt2-tinystories-generator)
-
-### Usage Instructions
-1. Visit the **Hugging Face Space** linked above.
-2. Enter a prompt (e.g., *"Once upon a time..."*) in the input field.
-3. Click **Generate** to receive an AI-generated short story.
-4. Modify the prompt and settings (temperature, top-k, top-p) for different results.
-
-## 📊 Training Details
-- **Model:** GPT-2 (124M)
-- **Dataset:** RonenEldan/TinyStories
-- **Training:** 3 epochs on Google Colab GPU (T4)
-- **Loss Metrics:**
-  - Training Loss: **3.08 → 2.86**
-  - Validation Loss: **1.46 → 1.40**
-
-## 📝 Evaluation & Observations
-- **Perplexity improved from 8.12 → 2.09**, indicating better text fluency.
-- **Validation loss decreased consistently**, suggesting effective generalization.
-- **Human evaluation highlighted minor inconsistencies**, such as abrupt scene shifts and simplistic narratives.
-
-## ⚖️ Ethical Considerations
-- **Bias Monitoring:** Pronoun analysis and diversity checks to ensure fairness.
-- **Harmful Content Mitigation:** Manually reviewed outputs for stereotypes.
-- **Text Processing Issues:** UTF-8 encoding applied to prevent character errors.
-
-## 🔮 Future Improvements
-- **Enhancing Creativity:** Fine-tune temperature and randomness settings.
-- **Genre-Specific Training:** Introduce theme-based datasets.
-- **Larger Model Training:** Experiment with **GPT-2 (355M)** for richer storytelling.
-
-## 🤝 Contributors
-- **Charla Pia Vella** (Project Developer)
-- **Affiliation:** ARI3333 Generative AI
-
-## 📜 License
-This project is released under the **Apache-2.0 License**.
-
-## 🎓 Acknowledgments
-- **OpenAI** for GPT-2
-- **Hugging Face** for the fine-tuning framework
-- **Ronen Eldan** for the TinyStories dataset
-
-📌 For more details, visit the [Hugging Face Space](https://huggingface.co/spaces/cpv2280/gpt2-tinystories-generator).
-