README.md

metadata

license: apache-2.0
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - jwang2373/UW-SBEL-ChronoGemma-27b-it
  - Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B
model-index:
  - name: Gemma2Crono-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: 70.86
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-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: 50.1
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-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: 23.72
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-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: 16.11
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-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: 16.05
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-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: 40.36
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=hotmailuser/Gemma2Crono-27B
          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:

• jwang2373/UW-SBEL-ChronoGemma-27b-it
• Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Saxo/Linkbricks-Horizon-AI-Korean-Superb-27B
  - model: jwang2373/UW-SBEL-ChronoGemma-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

Detailed results can be found here

Metric	Value
Avg.	36.20
IFEval (0-Shot)	70.86
BBH (3-Shot)	50.10
MATH Lvl 5 (4-Shot)	23.72
GPQA (0-shot)	16.11
MuSR (0-shot)	16.05
MMLU-PRO (5-shot)	40.36