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README.md
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# <span style="color: #7FFF7F;">LiveCC-7B-Instruct GGUF Models</span>
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## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
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Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
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✔ **Research** into ultra-low-bit quantization
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## **Choosing the Right Model Format**
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Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
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# <span style="color: #7FFF7F;">LiveCC-7B-Instruct GGUF Models</span>
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## <span style="color: #7F7FFF;">Model Generation Details</span>
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This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`e291450`](https://github.com/ggerganov/llama.cpp/commit/e291450b7602d7a36239e4ceeece37625f838373).
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## <span style="color: #7FFF7F;">Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)</span>
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Our latest quantization method introduces **precision-adaptive quantization** for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on **Llama-3-8B**. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.
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✔ **Research** into ultra-low-bit quantization
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## **Choosing the Right Model Format**
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Selecting the correct model format depends on your **hardware capabilities** and **memory constraints**.
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