---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- ifable/gemma-2-Ifable-9B
- jsgreenawalt/gemma-2-9B-it-advanced-v2.1
model-index:
- name: Gemma-2-Ataraxy-v2-9B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 21.36
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 39.8
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.83
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 12.3
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 4.88
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 35.79
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v2-9B
      name: Open LLM Leaderboard
---
# 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. 

![Ataraxy](https://i.imgur.com/P2F9XN9.png)

## About

In this merge, we stuck to using models that used preference optimized training (because, while very expensive to train, these are bar none the best performing Gemma finetunes in all my tests), or trained on the amazing gutenberg dataset just like the last one. You can read why jondurbin/gutenberg-dpo-v0.1 is such a good dataset here: https://huggingface.co/lemon07r/Gemma-2-Ataraxy-9B#why-gutenberg. 

This time we use the very good advanced 2.1 merge (a merge using the three best preference optimized models), and a new gutenberg model trained on the dataset in the style of SimPO. Both models alone were already better than the original Ataraxy at writing, and general use, which was a pretty high bar to clear. Merging good models, does not always mean a good resulting model. In fact, when the parent models are really good, usually the child model is not as good. This one however, has surprisingly done quite well in my testing thus far and should be a significant upgrade to the last 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.

## Benchmarks and Leaderboard Rankings

Coming soon.

## 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](https://github.com/cg123/mergekit).

The following models were included in the merge:
* [ifable/gemma-2-Ifable-9B](https://huggingface.co/ifable/gemma-2-Ifable-9B)
* [jsgreenawalt/gemma-2-9B-it-advanced-v2.1](https://huggingface.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lemon07r__Gemma-2-Ataraxy-v2-9B)

|      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|