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gemma-2-2b-jpn-it-GGUF

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GGUF
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@@ -38,18 +38,18 @@ Note that this model does not support a System prompt.
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  ELIZA-Tasks-100 is pretty standard benchmark for Japanese LLMs.
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  The perfect score is 5.00. As a reference, bartowski's gemma-2-27b-it.Q6_K.gguf scores 4.04.
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- | Filename | Quant type | File Size | Split | ELIZA-Tasks-100 | Nvidia 3090 | Description |
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- | -------- | ---------- | --------- | ----- | --------------- | ----------- | ----------- |
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- | [gemma-2-2b-jpn-it.f16.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.f16.gguf) | f16 | 5.24GB | false | 2.90 | 98t/s | Full F16 weights. |
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- | [gemma-2-2b-jpn-it.Q8_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q8_0.gguf) | Q8_0 | 2.78GB | false | 3.06 | 140t/s | Extremely high quality, *recommended*. |
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- | [gemma-2-2b-jpn-it-imatrix.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0.gguf) | Q4_0 | 1.63GB | false | 2.89 | 137t/s | Good quality, *recommended for edge device <8GB RAM*. |
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- | [gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | false | 2.78 | 2.79t/s | Good quality, *recommended for edge device <8GB RAM*. |
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- | [gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | false | 2.77 | 2.61t/s | Good quality, *recommended for edge device <8GB RAM*. |
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- | [gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | false | 2.65 | 3.09t/s | Good quality, *recommended for edge device <8GB RAM*. |
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- | [gemma-2-2b-jpn-it.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0.gguf) | Q4_0 | 1.63GB | false | 2.77 | 159t/s | Good quality, *recommended for edge device <8GB RAM* |
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- | [gemma-2-2b-jpn-it.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | false | 2.92 | 2.85t/s | Good quality, *recommended for edge device <8GB RAM* |
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- | [gemma-2-2b-jpn-it.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | false | 2.74 | 2.56t/s | Good quality, *recommended for edge device <8GB RAM* |
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- | [gemma-2-2b-jpn-it.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | false | 2.70 | 3.10t/s | Good quality, *recommended for edge device <8GB RAM*. |
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  ## How to check i8mm and sve support for ARM devices
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@@ -61,7 +61,7 @@ For ARM devices without both, it is recommended to use Q4_0_4_4.
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  With these support, the inference speed should be faster in the order of Q4_0_8_8 > Q4_0_4_8 > Q4_0_4_4 > Q4_0 without much effect on the quality of response.
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- This is a [list](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) of ARM devices that support different ARM instructions. Apparently, it is only a partial list. It is better you check for i8mm and sve support by yourself.
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  For Apple devices,
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@@ -90,9 +90,9 @@ On the other hand, Nvidia 3090 inference speed is significantly faster for Q4_0
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  | Google | Tensor | G1,G2 | No | No | Q4_0_4_4 |
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  | Google | Tensor | G3,G4 | Yes | Yes | Q4_0_8_8 |
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  | Samsung | Exynos | 2200,2400 | Yes | Yes | Q4_0_8_8 |
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- | Mediatek | Dimensity | 9000 | Yes | Yes | Q4_0_8_8 |
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  | Mediatek | Dimensity | 9300 | Yes | No | Q4_0_4_8 |
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- | Qualcomm | Snapdragon | 8 Gen 1 | Yes | Yes | Q4_0_8_8 |
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  | Qualcomm | Snapdragon | 8 Gen 2,8 Gen 3,X Elite | Yes | No | Q4_0_4_8 |
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  ## imatrix quantization
 
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  ELIZA-Tasks-100 is pretty standard benchmark for Japanese LLMs.
39
  The perfect score is 5.00. As a reference, bartowski's gemma-2-27b-it.Q6_K.gguf scores 4.04.
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+ | Filename | Quant type | File Size | ELIZA-Tasks-100 | Nvidia 3090 | Description |
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+ | -------- | ---------- | --------- | --------------- | ----------- | ----------- |
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+ | [gemma-2-2b-jpn-it.f16.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.f16.gguf) | f16 | 5.24GB | 2.90 | 98t/s | Full F16 weights. |
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+ | [gemma-2-2b-jpn-it.Q8_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q8_0.gguf) | Q8_0 | 2.78GB | 3.06 | 140t/s | Extremely high quality, *recommended*. |
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+ | [gemma-2-2b-jpn-it-imatrix.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0.gguf) | Q4_0 | 1.63GB | 2.89 | 137t/s | Good quality, *recommended for edge devices <8GB RAM*. |
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+ | [gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | 2.78 | 2.79t/s | Good quality, *recommended for edge devices <8GB RAM*. |
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+ | [gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | 2.77 | 2.61t/s | Good quality, *recommended for edge devices <8GB RAM*. |
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+ | [gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | 2.65 | 3.09t/s | Good quality, *recommended for edge devices <8GB RAM*. |
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+ | [gemma-2-2b-jpn-it.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0.gguf) | Q4_0 | 1.63GB | 2.77 | 159t/s | Good quality, *recommended for edge devices <8GB RAM* |
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+ | [gemma-2-2b-jpn-it.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | 2.92 | 2.85t/s | Good quality, *recommended for edge devices <8GB RAM* |
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+ | [gemma-2-2b-jpn-it.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | 2.74 | 2.56t/s | Good quality, *recommended for edge devices <8GB RAM* |
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+ | [gemma-2-2b-jpn-it.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | 2.70 | 3.10t/s | Good quality, *recommended for edge devices <8GB RAM*. |
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  ## How to check i8mm and sve support for ARM devices
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  With these support, the inference speed should be faster in the order of Q4_0_8_8 > Q4_0_4_8 > Q4_0_4_4 > Q4_0 without much effect on the quality of response.
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+ This is a [list](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) of ARM CPUs that support different ARM instructions. Another [list](https://raw.githubusercontent.com/ThomasKaiser/sbc-bench/refs/heads/master/sbc-bench.sh). Apparently, they only covers limited number of ARM CPUs. It is better you check for i8mm and sve support by yourself.
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  For Apple devices,
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  | Google | Tensor | G1,G2 | No | No | Q4_0_4_4 |
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  | Google | Tensor | G3,G4 | Yes | Yes | Q4_0_8_8 |
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  | Samsung | Exynos | 2200,2400 | Yes | Yes | Q4_0_8_8 |
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+ | Mediatek | Dimensity | 9000,9000+ | Yes | Yes | Q4_0_8_8 |
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  | Mediatek | Dimensity | 9300 | Yes | No | Q4_0_4_8 |
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+ | Qualcomm | Snapdragon | 7+ Gen 2,8/8+ Gen 1 | Yes | Yes | Q4_0_8_8 |
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  | Qualcomm | Snapdragon | 8 Gen 2,8 Gen 3,X Elite | Yes | No | Q4_0_4_8 |
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  ## imatrix quantization