Create README.md

README.md

@@ -1,42 +1,45 @@
-Model Card for Microsoft-phi-4-Instruct-AutoRound-GPTQ-4bit
-
-Model Overview
-
-Model Name: Microsoft-phi-4-Instruct-AutoRound-GPTQ-4bitModel Type: Instruction-tuned, Quantized GPT-4-based language modelQuantization: GPTQ 4-bitAuthor: Satwik11Hosted on: Hugging Face
-
-Description
+---
+license: mit
+language:
+- en
+base_model:
+- microsoft/phi-4
+new_version: microsoft/phi-4
+---
+# Model Card for Microsoft-phi-4-Instruct-AutoRound-GPTQ-4bit
+
+## Model Overview
+
+**Model Name**: Microsoft-phi-4-Instruct-AutoRound-GPTQ-4bit  
+**Model Type**: Instruction-tuned, Quantized GPT-4-based language model  
+**Quantization**: GPTQ 4-bit  
+**Author**: Satwik11  
+**Hosted on**: Hugging Face
+
+## Description
 
 This model is a quantized version of the Microsoft phi-4 Instruct model, designed to deliver high performance while maintaining computational efficiency. By leveraging the GPTQ 4-bit quantization method, it enables deployment in environments with limited resources while retaining a high degree of accuracy.
 
 The model is fine-tuned for instruction-following tasks, making it ideal for applications in conversational AI, question answering, and general-purpose text generation.
 
-Key Features
-
-Instruction-tuned: Fine-tuned to follow human-like instructions effectively.
-
-Quantized for Efficiency: Uses GPTQ 4-bit quantization to reduce memory requirements and inference latency.
-
-Pre-trained Base: Built on the Microsoft phi-4 framework, ensuring state-of-the-art performance on NLP tasks.
-
-Use Cases
-
-Chatbots and virtual assistants.
-
-Summarization and content generation.
-
-Research and educational applications.
-
-Semantic search and knowledge retrieval.
-
-Model Details
+## Key Features
 
-Architecture
+- **Instruction-tuned**: Fine-tuned to follow human-like instructions effectively.
+- **Quantized for Efficiency**: Uses GPTQ 4-bit quantization to reduce memory requirements and inference latency.
+- **Pre-trained Base**: Built on the Microsoft phi-4 framework, ensuring state-of-the-art performance on NLP tasks.
 
-Base Model: Microsoft phi-4
+## Use Cases
 
-Quantization Technique: GPTQ (4-bit)
+- Chatbots and virtual assistants.
+- Summarization and content generation.
+- Research and educational applications.
+- Semantic search and knowledge retrieval.
 
-Language: English
+## Model Details
 
-Training Objective: Instruction-following fine-tuning
+### Architecture
 
+- **Base Model**: Microsoft phi-4  
+- **Quantization Technique**: GPTQ (4-bit)  
+- **Language**: English  
+- **Training Objective**: Instruction-following fine-tuning