Create README.md

README.md

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+---
+tags:
+- javascript
+- code-generation
+- transformers
+- fine-tuned
+- distilgpt2
+license: mit
+library_name: transformers
+---
+
+# 🚀 DistilGPT-2 Code Generator (Explanation → JavaScript Code)
+
+This model is a **fine-tuned version of `distilgpt2`** trained to generate **JavaScript code** from natural language explanations.  
+
+It was trained on a dataset containing **explanation-code pairs**, making it useful for:  
+✅ **Code generation from text descriptions**  
+✅ **Learning JavaScript syntax & patterns**  
+✅ **Automated coding assistance**  
+
+---
+
+## **🛠 Model Details**
+- **Base Model:** `distilgpt2` (6x smaller than GPT-2)  
+- **Dataset:** JavaScript explanations + corresponding functions  
+- **Fine-tuning:** Trained using **LoRA (memory-efficient adaptation)**  
+- **Training Environment:** Google Colab (T4 GPU)  
+- **Optimization:** FP16 precision for faster training  
+
+---
+
+## **📊 Example Usage**
+Load the model and generate JavaScript code from explanations:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sureal01/distilgpt2-code-generator"  # Replace with your username
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def generate_code(explanation):
+    input_text = f"### Explanation:\n{explanation}\n\n### Generate JavaScript code:\n"
+    inputs = tokenizer(input_text, return_tensors="pt")
+
+    output = model.generate(**inputs, max_length=150, temperature=0.5, top_p=0.9, repetition_penalty=1.5)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Example
+test_explanation = "This function takes a name as input and returns a greeting message."
+generated_code = generate_code(test_explanation)
+print("\n🔹 **Generated Code:**\n", generated_code)