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README.md

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-license: mit
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+"""
+# AI FixCode Model 🛠️
+
+A Transformer-based code fixing model trained on diverse buggy → fixed code pairs. Built using [CodeT5](https://huggingface.co/Salesforce/codet5p-220m), this model identifies and corrects syntactic and semantic errors in source code.
+
+## 📌 Model Details
+- **Base Model**: `Salesforce/codet5p-220m`
+- **Type**: Seq2Seq (Encoder-Decoder)
+- **Trained On**: Custom dataset with real-world buggy → fixed examples.
+- **Languages**: Python (initially), can be expanded to JS, Go, etc.
+
+## 🔧 Intended Use
+Input a buggy function or script and receive a syntactically and semantically corrected version.
+
+**Example**:
+```python
+# Input:
+def add(x, y)
+ return x + y
+
+# Output:
+def add(x, y):
+    return x + y
+```
+
+## 🧠 How it Works
+The model learns from training examples that map erroneous code to corrected code. It uses token-level sequence generation to predict patches.
+
+## 🚀 Inference
+Use `transformers` pipeline or run via CLI:
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("YOUR_USERNAME/aifixcode-model")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/aifixcode-model")
+input_code = "def foo(x):\n print(x"
+inputs = tokenizer(input_code, return_tensors="pt")
+out = model.generate(**inputs, max_length=512)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+## 📂 Dataset Format
+```json
+[
+  {
+    "input": "def add(x, y)\n return x + y",
+    "output": "def add(x, y):\n    return x + y"
+  }
+]
+```
+
+## 🛡️ License
+MIT License
+
+## 🙏 Acknowledgements
+Built using 🤗 HuggingFace Transformers + Salesforce CodeT5.
+"""