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README.md

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+---
+tags:
+- code
+- python
+- code-generation
+- bug-injection
+- education
+license: mit
+---
+
+# Squash Code Corruptor Model
+
+T5-based model for generating realistic Python code bugs for educational purposes.
+
+## Model Description
+
+This model is trained to introduce realistic bugs into Python code, including:
+- Logic errors (operator swaps, off-by-one errors, wrong variables)
+- Syntax errors (missing colons, indentation issues)
+
+Trained on 1500 examples:
+- 1000 syntax error pairs
+- 500 logic error pairs (7 different categories)
+
+## Usage
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model = AutoModelForSeq2SeqLM.from_pretrained("onegaiosu/squash-code-corruptor")
+tokenizer = AutoTokenizer.from_pretrained("onegaiosu/squash-code-corruptor")
+
+# Corrupt code
+code = "def add(a, b):\n    return a + b"
+inputs = tokenizer(code, return_tensors="pt", max_length=512, truncation=True)
+outputs = model.generate(**inputs, max_length=512, temperature=0.8)
+corrupted = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+## Training Data
+
+Custom dataset of Python code pairs (correct → buggy) focusing on common programming mistakes
+for beginner and intermediate learners.
+
+## Intended Use
+
+Educational tool for the Squash app - helping students learn Python by fixing intentionally buggy code.
+
+## Limitations
+
+- Trained specifically on Python code
+- May not work well with very long or complex code snippets
+- Best for code snippets under 50 lines
+
+## Citation
+
+```
+@misc{squash-code-corruptor,
+  author = {Mao Abel},
+  title = {Squash Code Corruptor},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/onegaiosu/squash-code-corruptor}}
+}
+```