---
license: mit
language:
- en
base_model:
- 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

### Architecture

- **Base Model**: Microsoft phi-4  
- **Quantization Technique**: GPTQ (4-bit)  
- **Language**: English  
- **Training Objective**: Instruction-following fine-tuning