Triangle104/Gemma-2-Ataraxy-v2-9B-Q4_K_M-GGUF

This model was converted to GGUF format from lemon07r/Gemma-2-Ataraxy-v2-9B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

---

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.

Ataraxy 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:

ifable/gemma-2-Ifable-9B
jsgreenawalt/gemma-2-9B-it-advanced-v2.1

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.

---

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Gemma-2-Ataraxy-v2-9B-Q4_K_M-GGUF --hf-file gemma-2-ataraxy-v2-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Gemma-2-Ataraxy-v2-9B-Q4_K_M-GGUF --hf-file gemma-2-ataraxy-v2-9b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Gemma-2-Ataraxy-v2-9B-Q4_K_M-GGUF --hf-file gemma-2-ataraxy-v2-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Gemma-2-Ataraxy-v2-9B-Q4_K_M-GGUF --hf-file gemma-2-ataraxy-v2-9b-q4_k_m.gguf -c 2048