README.md

---
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)