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README.md

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-title: Qwen3.6 IQ2_M 2-Bit Quantized Model
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+title: IQ2_M - GeekedOut Quantizer
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 - gguf
 
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 - iq2-m
 
-- qwen
 
-- conversational
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+- quantization
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+- geeked-out
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 license: other
 
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-# Qwen3.6 - IQ2_M 2-Bit Quantized Model
 
 
 
-This repository contains a **Qwen3.6** large language model series that has been quantized to **IQ2_M (Intelligent Quants) 2-bit precision** using the GGUF format. The model retains high-quality performance while significantly reducing memory footprint through advanced mixed-precision quantization techniques.
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+# IQ2_M - GeekedOut Quantizer
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+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.
 
-## Model Details
 
-| Property | Value |
 
-|----------|-------|
 
-| Architecture | Qwen35Moe |
 
-| Context Length | 262,144 tokens |
 
-| Quantization Scheme | IQ2_M (2-bit) |
 
-| Format | GGUF (split across 2 files) |
+## About GeekedOut Quantizer
 
-| Total Parameters | ~35B (with MoE routing) |
 
-| File Size | ~8.3 GB + ~3.4 GB |
 
 
 
-## IQ2_M Quantization
+GeekedOut Quantizer is an advanced quantization framework designed to:
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+- 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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+## IQ2_M Quantization Features
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+The **IQ2_M** (Intelligent Quants) quantization scheme features:
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-The **IQ2_M** quantization scheme is part of the Intelligent Quants (IQ) family developed for efficient model inference:
 
 - Mixed precision - different weights receive varying bit allocations based on their sensitivity and importance
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 ## Supported Use Cases
 
-This quantized model is designed for:
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+GeekedOut Quantizer models are designed for:
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 - Conversational AI applications
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 ## Usage Instructions
 
-To load this model 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).
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+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).
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 ## Technical Notes
 
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 - IQ2_M quantization maintains conversational capability while achieving significant size reduction
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 - Compatible with llama.cpp, LM Studio, Jan, and other local inference frameworks
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 - Uses imatrix-based calibration for optimal quantization quality
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+- Developed by GeekedOut - focused on intelligent quantization methods
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