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README.md

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-license: apache-2.0
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+# 🧠 Phi-2 GPTQ (Quantized)
+
+This repository provides a 4-bit GPTQ quantized version of the **Phi-2** model by Microsoft, optimized for efficient inference using `gptqmodel`.
+
+## 📌 Model Details
+
+- **Base Model**: Microsoft Phi-2
+- **Quantization**: GPTQ (4-bit)
+- **Quantizer**: `GPTQModel`
+- **Framework**: PyTorch + HuggingFace Transformers
+- **Device Support**: CUDA (GPU)
+- **License**: Apache 2.0
+
+## 🚀 Features
+
+- ✅ Lightweight: 4-bit quantization significantly reduces memory usage
+- ✅ Fast Inference: Ideal for deployment on consumer GPUs
+- ✅ Compatible: Works with `transformers`, `optimum`, and `gptqmodel`
+- ✅ CUDA-accelerated: Automatically uses GPU for speed
+
+## 📚 Usage
+
+This model is ready-to-use with the Hugging Face `transformers` library.
+
+## 🧪 Intended Use
+
+- Research and development
+- Prototyping generative applications
+- Fast inference environments with limited GPU memory
+
+## 📖 References
+
+- Microsoft Phi-2: https://huggingface.co/microsoft/phi-2
+- GPTQModel: https://github.com/ModelCoud/GPTQModel
+- Transformers: https://github.com/huggingface/transformers
+
+## ⚖️ License
+
+This model is distributed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).