README.md

---
language:
- en
license: apache-2.0
tags:
- code
- code-review
- lora
- tinyllama
- python
- fine-tuned
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---

# AI Code Review Assistant — TinyLlama 1.1B (LoRA)

Fine-tuned version of TinyLlama 1.1B for automated Python code review, 
trained on CodeSearchNet using LoRA adapters.

## Model Details

- **Base model:** TinyLlama/TinyLlama-1.1B-Chat-v1.0
- **Fine-tuning method:** LoRA (r=16, alpha=32)
- **Trainable parameters:** 0.58% of total (42M / 7.3B)
- **Training data:** 10,000 Python functions from CodeSearchNet
- **Training time:** 32 minutes on Kaggle T4 GPU

## Evaluation Results

| Metric | Base Model | Fine-Tuned |
|--------|-----------|------------|
| ROUGE-L | 0.1541 | 0.5573 (+261%) |
| BERTScore F1 | 0.8226 | 0.9265 (+12.6%) |

## Usage
````python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    torch_dtype=torch.float32
)
model = PeftModel.from_pretrained(
    base_model,
    "Swarnimm22HF/ai-code-review-tinyllama"
)
tokenizer = AutoTokenizer.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
)

prompt = """### Instruction:
Review the following Python function.

### Code:
```python
def divide(a, b):
    return a / b
```

### Response:"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
````

## Full Project

GitHub: [AI Code Review Assistant](https://github.com/Swarnimm22/AI_Code_Review_Assistant)