metadata

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.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

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

Detailed results can be found here

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