| --- |
| language: en |
| license: mit |
| tags: |
| - code-review |
| - agent |
| - text-generation |
| - distilgpt2 |
| widget: |
| - text: "Review this code:\ndef get_user(id):\n return users[id]" |
| example_title: "Missing null check" |
| - text: "Improve this loop:\nfor i in range(len(items)):\n process(items[i])" |
| example_title: "Inefficient loop" |
| - text: "Fix this average calculation:\ndef avg(data):\n return sum(data)/len(data)" |
| example_title: "Division by zero" |
| --- |
| |
| # Code Review Agent Model |
|
|
| This is a lightweight language model (based on `distilgpt2`) designed to generate helpful code review comments. It is a starting point for building an AI that reviews pull requests, suggests improvements, and catches common bugs. |
|
|
| ## Model Details |
|
|
| - **Base model**: [distilgpt2](https://huggingface.co/distilgpt2) – a distilled version of GPT-2 with 82M parameters. |
| - **Language**: English |
| - **Intended use**: Generating code review comments, suggesting fixes, and answering questions about code. |
| - **Limitations**: The model has not been fine‑tuned on code‑review data; it may produce generic or off‑topic responses. Fine‑tuning on a dataset of real code reviews would greatly improve its utility. |
|
|
| ## How to Use |
|
|
| ### With transformers pipeline |
|
|
| ```python |
| from transformers import pipeline |
| |
| pipe = pipeline("text-generation", model="your-username/code-review-agent") |
| |
| code = "def get_user(id):\n return users[id] # missing null check" |
| prompt = f"Review this code:\n{code}\nComment:" |
| result = pipe(prompt, max_new_tokens=100, do_sample=True, temperature=0.7) |
| print(result[0]['generated_text']) |
