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README.md

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+---
+language: en
+license: mit
+tags:
+  - text-generation
+  - gpt2
+  - causal-lm
+  - shakespeare
+  - small-model
+---
+
+# 🧠 SLM-GPT2: Tiny Shakespeare GPT-2 Model
+
+`SLM-GPT2` is a small GPT-2-like language model trained from scratch on the [Tiny Shakespeare dataset](https://huggingface.co/datasets/tiny_shakespeare). It’s a toy model meant for educational purposes, experimentation, and understanding how transformer-based language models work.
+
+---
+
+## ✨ Model Details
+
+- **Architecture**: GPT-2 (custom config)
+- **Layers**: 4
+- **Hidden size**: 256
+- **Heads**: 4
+- **Max sequence length**: 128
+- **Vocabulary size**: Same as tokenizer (based on `distilgpt2` or custom)
+- **Training epochs**: 3
+- **Dataset**: [tiny_shakespeare](https://huggingface.co/datasets/tiny_shakespeare)
+
+---
+
+## 🧪 Intended Use
+
+- Educational demos
+- Debugging/training pipeline validation
+- Low-resource inference tests
+- Not suitable for production or accurate text generation
+
+---
+
+## 🚫 Limitations
+
+- Trained on a tiny dataset (~100 KB)
+- Limited vocabulary and generalization
+- Can generate incoherent or biased outputs
+- Not safe for deployment in real-world applications
+
+---
+
+## 💻 How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+model = AutoModelForCausalLM.from_pretrained("your-username/slm-gpt2")
+tokenizer = AutoTokenizer.from_pretrained("your-username/slm-gpt2")
+
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+output = generator("To be or not to be", max_length=50)
+print(output[0]['generated_text'])