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README.md

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 license: openrail
 short_description: Fine-tuned Llama-3 for generating Python unit tests
 ---
+# 🧪 AI Unit Test Generator (Fine-Tuned Llama-3)
 
+This application uses a fine-tuned version of **Meta's Llama-3-8B** to automatically generate `pytest` unit tests for Python functions. It is designed to help developers speed up Test-Driven Development (TDD) by automating the boilerplate code for testing.
+
+## 🚀 How to Use
+1.  **Paste your Python function** into the input box (e.g., a function that adds two numbers or handles a database connection).
+2.  Click **"Generate Pytest"**.
+3.  The model will output a complete, runnable `pytest` code block, including edge cases and assertions.
+
+## 🔧 Technical Stack
+This project serves as a demonstration of modern MLOps and PEFT (Parameter-Efficient Fine-Tuning) techniques:
+
+* **Base Model:** `unsloth/llama-3-8b-bnb-4bit` (Quantized for efficiency)
+* **Fine-Tuning:** Trained on the **Alpaca-Python-18k** dataset using **QLoRA** (Low-Rank Adaptation) adapters.
+* **Optimization:** Utilized **Unsloth** for 2x faster training and optimized inference memory usage.
+* **Infrastructure:**
+    * **Training:** Google Colab (T4 GPU) with Weights & Biases logging.
+    * **Inference:** Hugging Face Spaces (CPU/GPU) via `peft` and `gradio`.
+
+## 📊 Model Performance
+The model was fine-tuned to strictly follow the "Instruction-Input-Response" format, minimizing hallucination and ensuring that generated tests match the syntax and logic of the input function.
+
+---
+*Built by [Nihar Donthireddy](https://www.linkedin.com/in/nihar-donthireddy-048b96277/)*
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference