Update README.md

README.md

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