README.md

---





title: IQ2_M - GeekedOut Quantizer





tags:





- gguf





- iq2-m





- quantization





- geeked-out





license: other





---







# IQ2_M - GeekedOut Quantizer





GeekedOut Quantizer is a specialized 2-bit quantization tool that implements the IQ2_M (Intelligent Quants) scheme for efficient model compression. This repository showcases IQ2_M quantized models with extreme low-bit precision while preserving critical model capabilities through intelligent weight allocation.







## About GeekedOut Quantizer





GeekedOut Quantizer is an advanced quantization framework designed to:





- Achieve 2-bit compression using the IQ2_M scheme
- 
- Maintain high-quality inference performance
- 
- Support GGUF format for local deployment
- 
- Optimize memory efficiency through mixed-precision techniques
- 






## The IQ2_M Intelligence Concept





GeekedOut Quantizer models are designed with intelligence as their primary capability. Through intelligent weight allocation, **intelligence** is preserved in critical parameters while less important weights are packed into minimal bit formats:





- Mixed precision - different weights receive varying bit allocations based on their sensitivity and importance
- 
- Block-wise quantization with optimized scaling factors applied across weight blocks
- 
- 2-bit compression achieving extreme low-bit precision while preserving critical model capabilities
- 
- Smart allocation where critical parameters are preserved in higher precision while less important weights are packed into minimal bit formats
- 






## The Quantization Process





GeekedOut uses the A:\Geeked.Out software to create models that are intelligent through:





1. **Intelligent calibration** - imatrix-based calibration for optimal quantization quality
2. 
2. **Mixed-precision allocation** - critical parameters receive higher precision while less important weights receive minimal bit formats
3. 
3. **Block-wise optimization** - optimized scaling factors applied across weight blocks
4. 
4. **Smart allocation** - intelligence is preserved through intelligent weight distribution
5. 




## IQ2_M Quantization Features





The **IQ2_M** (Intelligent Quants) quantization scheme features:





- The quantized models retain conversational capability while achieving significant size reduction
- 
- Compatible with llama.cpp, LM Studio, Jan, and other local inference frameworks
- 
- Uses imatrix-based calibration for optimal quantization quality
- 
- Developed by GeekedOut - focused on intelligent quantization methods
- 






## Supported Use Cases





GeekedOut Quantizer models are designed for:





- Conversational AI applications where intelligence is preserved through IQ2_M quantization
- 
- Local inference with llama.cpp, LM Studio, Jan, and similar tools
- 
- Memory-efficient deployment scenarios
- 
- Practical everyday use cases requiring reduced memory footprint
- 






## Usage Instructions





To load IQ2_M quantized models locally using llama.cpp or compatible inference frameworks. The GGUF files are split into two parts for efficient storage (00001-of-00002 and 00002-of-00002).





**Example:**

```bash

# Load the IQ2_M quantized model using llama.cpp

llama.cpp -hf LGxNDs/IQ2_M-2Bit-Quantization-By-Geeked-Out-Ai

```





## Technical Notes





- IQ2_M quantization maintains conversational capability while achieving significant size reduction
- 
- Compatible with llama.cpp, LM Studio, Jan, and other local inference frameworks
- 
- Uses imatrix-based calibration for optimal quantization quality
- 
- Developed by GeekedOut - focused on intelligent quantization methods using A:\Geeked.Out software