QuantFactory/Gemma-2-Ataraxy-v2-9B-GGUF
This is quantized version of lemon07r/Gemma-2-Ataraxy-v2-9B created using llama.cpp
Original Model Card
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 but using better models this time.
GGUF / EXL2 Quants
Bartowski quants (imatrix): https://huggingface.co/bartowski/Gemma-2-Ataraxy-v2-9B-GGUF
Mradermacher quants (static): https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v2-9B-GGUF
Mradermacher quants (imatrix): https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v2-9B-i1-GGUF
Bartowski and mradermacher use different calibration data for their imatrix quants I believe, and the static quant of course uses none. Pick your poison.
More coming soon.
Format
Use Gemma 2 format.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
This is a merge of pre-trained language models created using mergekit.
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: ifable/gemma-2-Ifable-9B
dtype: bfloat16
merge_method: slerp
parameters:
t:
- 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
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 |
Second highest ranked open weight model in EQ-Bench.
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard21.360
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard39.800
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard0.830
- acc_norm on GPQA (0-shot)Open LLM Leaderboard12.300
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.880
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard35.790