Update README.md

README.md

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 This model was converted to GGUF format from [`lemon07r/Gemma-2-Ataraxy-v2-9B`](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v2-9B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v2-9B) for more details on the model.
 
+---
+Model details:
+-
+Model details:
+
+Gemma 2 Ataraxy v2 9B
+
+Finally, after much testing, a sucessor to the first Gemma 2 Ataraxy 9B. Same kind of recipe, using the same principles, same concept as the last Ataraxy. It's not quite a better overall model, v1 is more well rounded, v2 is a little better at writing but has a little more slop and some other issues. consider this a sidegrade.
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+Ataraxy GGUF / EXL2 Quants
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+Bartowski quants (imatrix): https://huggingface.co/bartowski/Gemma-2-Ataraxy-v2-9B-GGUF
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+Mradermacher quants (static): https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v2-9B-GGUF
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+Mradermacher quants (imatrix): https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v2-9B-i1-GGUF
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+Bartowski and mradermacher use different calibration data for their imatrix quants I believe, and the static quant of course uses none. Pick your poison.
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+More coming soon. Format
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+Use Gemma 2 format. Merge Details Merge Method
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+This model was merged using the SLERP merge method. Models Merged
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+This is a merge of pre-trained language models created using mergekit.
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+The following models were included in the merge:
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+ifable/gemma-2-Ifable-9B
+jsgreenawalt/gemma-2-9B-it-advanced-v2.1
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+Configuration
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+The following YAML configuration was used to produce this model:
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+base_model: ifable/gemma-2-Ifable-9B dtype: bfloat16 merge_method: slerp parameters: t:
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+    filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0]
+    filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0]
+    value: 0.5 slices:
+    sources:
+        layer_range: [0, 42] model: jsgreenawalt/gemma-2-9B-it-advanced-v2.1
+        layer_range: [0, 42] model: ifable/gemma-2-Ifable-9B
+
+Open LLM Leaderboard Evaluation Results
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+Detailed results can be found here Metric Value Avg. 19.16 IFEval (0-Shot) 21.36 BBH (3-Shot) 39.80 MATH Lvl 5 (4-Shot) 0.83 GPQA (0-shot) 12.30 MuSR (0-shot) 4.88 MMLU-PRO (5-shot) 35.79
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+Second highest ranked open weight model in EQ-Bench.
+
+---
+
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)