Pradnya27
/

codegpt-lora-code-generation

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 ---
+language: en
+license: mit
+tags:
+- code-generation
+- python
+- lora
+- peft
+- causal-lm
+base_model: microsoft/CodeGPT-small-py
 ---
+# CodeGPT LoRA Fine-tuned for Code Generation
+Fine-tuned version of CodeGPT using LoRA (Low-Rank Adaptation) for Python code generation.
+## 🔗 Links
+- **Live Demo:** https://huggingface.co/spaces/Pradnya27/code-generator
+- **Full Fine-tuned Model:** https://huggingface.co/Pradnya27/codegpt-finetuned-code-generation
+- **GitHub:** https://github.com/pradnyagundu/codegpt-finetuned-code-generation
 ## Model Details
+- **Base model:** microsoft/CodeGPT-small-py (124M parameters)
+- **Method:** LoRA (Low-Rank Adaptation)
+- **Trainable parameters:** 589,824 (0.36% of total)
+- **Model size:** 2.36MB (vs 651MB for full fine-tuning)
+- **Dataset:** Rabinovich/Code-Generation-LLM-LoRA (5000 examples)
 ## Training Details
+- **Epochs:** 2
+- **Learning rate:** 3e-4
+- **Batch size:** 8
+- **LoRA rank:** 16
+- **LoRA alpha:** 32
+- **Hardware:** Google Colab T4 GPU
+- **Training time:** ~9 minutes
+## Training Results
+| Step | Loss |
+|------|------|
+| 100 | 4.28 |
+| 300 | 3.45 |
+| 500 | 3.28 |
+| 700 | 3.24 |
+| 900 | 3.15 |
+| 1200 | 3.14 |
+## Comparison vs Full Fine-tuning
+| | Full Fine-tune | LoRA |
+|---|---|---|
+| Final loss | 2.31 | 3.14 |
+| Model size | 651MB | 2.36MB |
+| Training time | ~14 min | ~9 min |
+| Trainable params | 124M | 589K |
+## How to Use
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/CodeGPT-small-py")
+model = PeftModel.from_pretrained(base_model, "Pradnya27/codegpt-lora-code-generation")
+tokenizer = AutoTokenizer.from_pretrained("microsoft/CodeGPT-small-py")
+tokenizer.pad_token = tokenizer.eos_token
+prompt = "Generate code: Write a function to check if a number is prime"
+inputs = tokenizer(prompt, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(inputs["input_ids"], max_new_tokens=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Limitations
+- Trained on competitive programming problems — works best for algorithmic tasks
+- Small base model (124M params) limits output quality
+- Full fine-tuning achieves lower loss on this dataset
+## Future Work
+- Train on full 34K dataset
+- Increase LoRA rank to r=32 or r=64
+- Evaluate on HumanEval benchmark