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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
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- Maintain high-quality inference performance
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- Support GGUF format for local deployment
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- Optimize memory efficiency through mixed-precision techniques
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## 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
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- Block-wise quantization with optimized scaling factors applied across weight blocks
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- 2-bit compression achieving extreme low-bit precision while preserving critical model capabilities
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- Smart allocation where critical parameters are preserved in higher precision while less important weights are packed into minimal bit formats
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## 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
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- Local inference with llama.cpp, LM Studio, Jan, and similar tools
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- Memory-efficient deployment scenarios
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- Practical everyday use cases requiring reduced memory footprint
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## 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
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