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README.md
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@@ -17,6 +17,12 @@ base_model: google/gemma-3-1b-it-qat-q4_0-unquantized
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## Llamacpp imatrix Quantizations of gemma-3-1b-it-qat by google
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5147">b5147</a> for quantization.
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Original model: https://huggingface.co/google/gemma-3-1b-it-qat-q4_0-unquantized
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## Llamacpp imatrix Quantizations of gemma-3-1b-it-qat by google
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These are derived from the QAT (quantized aware training) weights provided by Google
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*ONLY* Q4_0 is expected to be better, but figured while I'm at it I might as well make others to see what happens?
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[gemma-3-1b-it-qat-Q4_0.gguf](https://huggingface.co/bartowski/google_gemma-3-1b-it-qat-GGUF/blob/main/google_gemma-3-1b-it-qat-Q4_0.gguf) | Q4_0 | 0.72GB | Should be improved due to QAT, offers online repacking for ARM and AVX CPU inference.
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5147">b5147</a> for quantization.
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Original model: https://huggingface.co/google/gemma-3-1b-it-qat-q4_0-unquantized
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