khulnasoft/aifixcode-model

The model in the provided documentation is called AI FixCode. It's a Transformer-based model built on the CodeT5 architecture, and its purpose is to automatically fix errors in source code. It's an encoder-decoder (sequence-to-sequence) model designed primarily for Python, with future plans for other languages.

The following documentation has been improved for clarity, structure, and conciseness, while providing additional technical details for a more professional and informative tone.

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Model: AI FixCode

License	Base Model	Tags	Datasets	Metrics
MIT	Salesforce/codet5p-220m	code-repair, code-generation, text2text-generation, code-correction	nvidia/OpenCodeReasoning, future-technologies/Universal-Transformers-Dataset	BLEU

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AI FixCode is a specialized Transformer-based model built upon the CodeT5 architecture for the purpose of automated source code repair. Operating as a sequence-to-sequence encoder-decoder model, it's designed to accept buggy code as input and generate a corrected version as output. It is currently optimized for Python and addresses both syntactic and semantic errors. This model is ideal for integration into development environments and CI/CD pipelines to streamline debugging.

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How It Works

AI FixCode functions as a sequence-to-sequence (seq2seq) system, mapping an input sequence of "buggy" code tokens to an output sequence of "fixed" code tokens. During training, the model learns to identify and predict the necessary code transformations by being exposed to a vast number of faulty and corrected code pairs. This process allows it to generalize and correct a wide range of code issues, from minor syntax errors (e.g., missing colons) to more complex logical (semantic) bugs. The model's encoder processes the input code to create a contextual representation, and the decoder uses this representation to generate the corrected code.

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Training and Usage

The model was trained on a custom dataset of structured buggy-to-fixed code pairs. Each pair is a JSON object with "input" for the faulty code and "output" for the corrected code. This supervised learning approach allows the model to learn the specific mappings required for code repair.

Usage Example

The following Python example demonstrates how to use the model with the Hugging Face transformers library. The process involves loading the model, tokenizing the input, generating the corrected output, and decoding the result.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# 1. Load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("path/to/ai-fixcode")
model = AutoModelForSeq2SeqLM.from_pretrained("path/to/ai-fixcode")

# 2. Tokenize the input code snippet
buggy_code = """
def add(x, y)
    return x + y
"""
inputs = tokenizer(buggy_code, return_tensors="pt")

# 3. Generate the corrected code
outputs = model.generate(inputs.input_ids, max_length=128)

# 4. Decode the output tokens back into a string
corrected_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

# The corrected output will be:
# def add(x, y):
#     return x + y
print(corrected_code)