---
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](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:
* [BarraHome/Mistroll-7B-v2.2](https://huggingface.co/BarraHome/Mistroll-7B-v2.2)
* [ClaudioItaly/Evolutionstory](https://huggingface.co/ClaudioItaly/Evolutionstory)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ClaudioItaly__Evolutionstory-7B-v2.2)

|      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|