Update README.md

README.md

@@ -1,36 +1,47 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: distilgpt2
+language: 
+- en
 tags:
-- generated_from_trainer
+- text-generation
+- gpt2
+- tiny-stories
+- creative-writing
+license: mit
+datasets:
+- roneneldan/TinyStories
+metrics:
+- perplexity
 model-index:
 - name: tiny-stories-gpt2
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# 🧚‍♀️ Tiny Stories GPT-2
+**Author:** [Fathi7ma](https://huggingface.co/Fathi7ma)  
+**Model type:** Fine-tuned GPT-2 for creative story generation  
+**Dataset:** [roneneldan/TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)
 
-# tiny-stories-gpt2
-
-This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.6995
-
-## Model description
-
-More information needed
+---
 
-## Intended uses & limitations
+## 🧠 Model Overview
+This model is a fine-tuned version of **GPT-2 (small)** trained on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset — a large collection of short, simple stories for children.  
+The goal of this project is to create a lightweight, fun, and creative **story generator** that can produce short, imaginative stories from user prompts.
 
-More information needed
+---
 
-## Training and evaluation data
+## ✨ Intended Use
+Use this model for:
+- Generating **short, creative stories**
+- Educational or entertainment purposes  
+- Quick writing inspiration or kids’ content
 
-More information needed
+Example use:
+```python
+from transformers import pipeline
+generator = pipeline("text-generation", model="Fathi7ma/tiny-stories-gpt2")
+prompt = "A tiny robot who wanted to fly"
+print(generator(prompt, max_length=80, num_return_sequences=1)[0]["generated_text"])
 
-## Training procedure
 
 ### Training hyperparameters