---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- recoilme/recoilme-gemma-2-9B-v0.4
- nbeerbower/Gemma2-Gutenberg-Doppel-9B
model-index:
- name: recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
  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: 76.15
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      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: 43.94
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      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: 6.34
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      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: 12.19
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      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: 13.31
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      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: 36.84
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=zelk12/recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1
      name: Open LLM Leaderboard
---
# merge

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [recoilme/recoilme-gemma-2-9B-v0.4](https://huggingface.co/recoilme/recoilme-gemma-2-9B-v0.4)
* [nbeerbower/Gemma2-Gutenberg-Doppel-9B](https://huggingface.co/nbeerbower/Gemma2-Gutenberg-Doppel-9B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: recoilme/recoilme-gemma-2-9B-v0.4
  - model: nbeerbower/Gemma2-Gutenberg-Doppel-9B
merge_method: slerp
base_model: recoilme/recoilme-gemma-2-9B-v0.4
dtype: bfloat16
parameters:
  t: 0.5

```

# [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_zelk12__recoilme-gemma-2-Gutenberg-Doppel-9B-v0.1)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |31.46|
|IFEval (0-Shot)    |76.15|
|BBH (3-Shot)       |43.94|
|MATH Lvl 5 (4-Shot)| 6.34|
|GPQA (0-shot)      |12.19|
|MuSR (0-shot)      |13.31|
|MMLU-PRO (5-shot)  |36.84|