metadata

license: mit
library_name: transformers
tags:
  - mergekit
  - merge
base_model:
  - BarraHome/Mistroll-7B-v2.2
  - ClaudioItaly/Evolutionstory
model-index:
  - name: Evolutionstory-7B-v2.2
    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: 48.14
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          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: 31.62
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          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: 6.42
            name: exact match
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          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: 3.36
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          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: 10.66
            name: acc_norm
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          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: 23.99
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=ClaudioItaly/Evolutionstory-7B-v2.2
          name: Open LLM Leaderboard

merge

I finally think I managed to make a model with high writing skills. Based on the prompt it manages to be very coherent with the story.

It also has great RAG capabilities.

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: BarraHome/Mistroll-7B-v2.2
  - model: ClaudioItaly/Evolutionstory
merge_method: slerp
base_model: ClaudioItaly/Evolutionstory
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.	20.70
IFEval (0-Shot)	48.14
BBH (3-Shot)	31.62
MATH Lvl 5 (4-Shot)	6.42
GPQA (0-shot)	3.36
MuSR (0-shot)	10.66
MMLU-PRO (5-shot)	23.99