π DistilGPT-2 Code Generator (Explanation β 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:
β
Code generation from text descriptions
β
Learning JavaScript syntax & patterns
β
Automated coding assistance
π 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
π Example Usage
Load the model and generate JavaScript code from explanations:
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("\nπΉ **Generated Code:**\n", generated_code)
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