README.md

---
license: cc-by-nc-sa-4.0
library_name: transformers
tags:
- mergekit
- merge
- conversational
base_model:
- rombodawg/Rombos-LLM-V2.5-Qwen-7b
- Cran-May/T.E-8.1
model-index:
- name: Rombos-2.5-T.E-8.1
  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: 69.25
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      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: 36.5
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      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: 0.83
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      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: 8.17
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      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: 10.98
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      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: 38.28
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=suayptalha/Rombos-2.5-T.E-8.1
      name: Open LLM Leaderboard
---
# merged

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

You can use ChatML format.

### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [rombodawg/Rombos-LLM-V2.5-Qwen-7b](https://huggingface.co/rombodawg/Rombos-LLM-V2.5-Qwen-7b)
* [Cran-May/T.E-8.1](https://huggingface.co/Cran-May/T.E-8.1)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
dtype: bfloat16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 28]
    model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
  - layer_range: [0, 28]
    model: Cran-May/T.E-8.1
```
# [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_suayptalha__Rombos-2.5-T.E-8.1)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |27.34|
|IFEval (0-Shot)    |69.25|
|BBH (3-Shot)       |36.50|
|MATH Lvl 5 (4-Shot)| 0.83|
|GPQA (0-shot)      | 8.17|
|MuSR (0-shot)      |10.98|
|MMLU-PRO (5-shot)  |38.28|