datasets:
nvidia/OpenCodeReasoning
future-technologies/Universal-Transformers-Dataset metrics:
bleu
AI FixCode: A Code Repair Model 🛠️ AI FixCode is a Transformer-based model designed to automatically identify and correct errors in source code. Built upon the powerful CodeT5 architecture, it's trained on a diverse dataset of real-world buggy and fixed code pairs to address both syntactic and semantic issues.
📌 Key Features Base Model: Salesforce/codet5p-220m
Architecture: Encoder-Decoder (Seq2Seq)
Target Languages: Primarily Python, with future plans to expand to other languages like JavaScript and Go.
Task: Code repair and error correction.
🔧 How to Use Simply provide a faulty code snippet, and the model will return a corrected version. It's intended for use in code editors, IDEs, or automated pipelines to assist developers in debugging.
Example:
Input:
def add(x, y) return x + y
Output:
def add(x, y): return x + y
🧠 Under the Hood
The model operates as a sequence-to-sequence system. During training, it learns to map a sequence of buggy code tokens to a sequence of correct code tokens. This approach allows it to "reason" about the necessary changes at a granular level, effectively predicting the patches needed to fix the code.
🚀 Getting Started with Inference You can easily use the model with the Hugging Face transformers library.
Python Code
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "khulnasoft/aifixcode-model" # Replace with your model name tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_code = "def foo(x):\n print(x" inputs = tokenizer(input_code, return_tensors="pt")
Generate the corrected code
outputs = model.generate(**inputs, max_length=512) corrected_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(corrected_code)
📂 Dataset and Format The model was trained on a custom dataset following a simple format:
[ { "input": "def add(x, y)\n return x + y", "output": "def add(x, y):\n return x + y" }, { "input": "for i in range(10): \n if i == 5 \n print(i)", "output": "for i in range(10): \n if i == 5: \n print(i)" } ]
This format allows the model to learn the direct mapping between erroneous and corrected code.
🛡️ License and Acknowledgements License: MIT License
Acknowledgements: This project was made possible by the incredible work of the Hugging Face team for their Transformers library and Salesforce for the CodeT5 model.