README.md

---
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model:
- Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B
- piotr25691/SystemGemma2-27b-it
model-index:
- name: Gemma2Slerp2-27B
  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: 75.46
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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: 51.09
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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: 24.32
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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: 16.0
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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: 16.93
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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: 40.25
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=allknowingroger/Gemma2Slerp2-27B
      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:
* [Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B)
* [piotr25691/SystemGemma2-27b-it](https://huggingface.co/piotr25691/SystemGemma2-27b-it)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B
  - model: piotr25691/SystemGemma2-27b-it
merge_method: slerp
base_model: Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers

```
# [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_allknowingroger__Gemma2Slerp2-27B)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |37.34|
|IFEval (0-Shot)    |75.46|
|BBH (3-Shot)       |51.09|
|MATH Lvl 5 (4-Shot)|24.32|
|GPQA (0-shot)      |16.00|
|MuSR (0-shot)      |16.93|
|MMLU-PRO (5-shot)  |40.25|