---
license: apache-2.0
tags:
- merge
- mergekit
---

# MetaModel

This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit):
 * [jeonsworld/CarbonVillain-en-10.7B-v4](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4)
 * [kekmodel/StopCarbon-10.7B-v5](https://huggingface.co/kekmodel/StopCarbon-10.7B-v5)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: jeonsworld/CarbonVillain-en-10.7B-v4
        layer_range: [0, 48]
      - model: kekmodel/StopCarbon-10.7B-v5
        layer_range: [0, 48]
merge_method: slerp
base_model: jeonsworld/CarbonVillain-en-10.7B-v4
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```

# Dataset Card for Evaluation run of gagan3012/MetaModel

<!-- Provide a quick summary of the dataset. -->

Dataset automatically created during the evaluation run of model [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).

The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.

The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.

An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).

To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_gagan3012__MetaModel",
	"harness_winogrande_5",
	split="train")
```

## Latest results

These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel/blob/main/results_2024-01-04T14-09-43.780941.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):

```python
{
    "all": {
        "acc": 0.6664380298886512,
        "acc_stderr": 0.031642195230944255,
        "acc_norm": 0.6671639222858992,
        "acc_norm_stderr": 0.03228745343467652,
        "mc1": 0.5691554467564259,
        "mc1_stderr": 0.01733527247533237,
        "mc2": 0.7184177934834866,
        "mc2_stderr": 0.014995634120330182
    },
    "harness|arc:challenge|25": {
        "acc": 0.6843003412969283,
        "acc_stderr": 0.013582571095815291,
        "acc_norm": 0.7107508532423208,
        "acc_norm_stderr": 0.01325001257939344
    },
    "harness|hellaswag|10": {
        "acc": 0.7132045409281019,
        "acc_stderr": 0.004513409114983828,
        "acc_norm": 0.8844851623182632,
        "acc_norm_stderr": 0.0031898897894046684
    },
    "harness|hendrycksTest-abstract_algebra|5": {
        "acc": 0.43,
        "acc_stderr": 0.049756985195624284,
        "acc_norm": 0.43,
        "acc_norm_stderr": 0.049756985195624284
    },
    "harness|hendrycksTest-anatomy|5": {
        "acc": 0.6148148148148148,
        "acc_stderr": 0.04203921040156279,
        "acc_norm": 0.6148148148148148,
        "acc_norm_stderr": 0.04203921040156279
    },
    "harness|hendrycksTest-astronomy|5": {
        "acc": 0.743421052631579,
        "acc_stderr": 0.0355418036802569,
        "acc_norm": 0.743421052631579,
        "acc_norm_stderr": 0.0355418036802569
    },
    "harness|hendrycksTest-business_ethics|5": {
        "acc": 0.75,
        "acc_stderr": 0.04351941398892446,
        "acc_norm": 0.75,
        "acc_norm_stderr": 0.04351941398892446
    },
    "harness|hendrycksTest-clinical_knowledge|5": {
        "acc": 0.6830188679245283,
        "acc_stderr": 0.02863723563980089,
        "acc_norm": 0.6830188679245283,
        "acc_norm_stderr": 0.02863723563980089
    },
    "harness|hendrycksTest-college_biology|5": {
        "acc": 0.7638888888888888,
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        "acc_norm_stderr": 0.03551446610810826
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    "harness|hendrycksTest-college_chemistry|5": {
        "acc": 0.47,
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        "acc_norm": 0.47,
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    },
    "harness|hendrycksTest-college_computer_science|5": {
        "acc": 0.48,
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        "acc_norm": 0.48,
        "acc_norm_stderr": 0.05021167315686781
    },
    "harness|hendrycksTest-college_mathematics|5": {
        "acc": 0.32,
        "acc_stderr": 0.046882617226215034,
        "acc_norm": 0.32,
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    },
    "harness|hendrycksTest-college_medicine|5": {
        "acc": 0.6647398843930635,
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        "acc_norm_stderr": 0.03599586301247077
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    "harness|hendrycksTest-college_physics|5": {
        "acc": 0.38235294117647056,
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        "acc_norm_stderr": 0.04835503696107223
    },
    "harness|hendrycksTest-computer_security|5": {
        "acc": 0.75,
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        "acc_norm": 0.75,
        "acc_norm_stderr": 0.04351941398892446
    },
    "harness|hendrycksTest-conceptual_physics|5": {
        "acc": 0.625531914893617,
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    "harness|hendrycksTest-econometrics|5": {
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    "harness|hendrycksTest-electrical_engineering|5": {
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    "harness|hendrycksTest-elementary_mathematics|5": {
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    },
    "harness|hendrycksTest-formal_logic|5": {
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    "harness|hendrycksTest-global_facts|5": {
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    "harness|hendrycksTest-high_school_biology|5": {
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    "harness|hendrycksTest-high_school_chemistry|5": {
        "acc": 0.5073891625615764,
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    "harness|hendrycksTest-high_school_computer_science|5": {
        "acc": 0.72,
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        "acc_norm": 0.72,
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    },
    "harness|hendrycksTest-high_school_european_history|5": {
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    },
    "harness|hendrycksTest-high_school_geography|5": {
        "acc": 0.8636363636363636,
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    },
    "harness|hendrycksTest-high_school_government_and_politics|5": {
        "acc": 0.8963730569948186,
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        "acc_norm": 0.8963730569948186,
        "acc_norm_stderr": 0.021995311963644244
    },
    "harness|hendrycksTest-high_school_macroeconomics|5": {
        "acc": 0.6692307692307692,
        "acc_stderr": 0.02385479568097114,
        "acc_norm": 0.6692307692307692,
        "acc_norm_stderr": 0.02385479568097114
    },
    "harness|hendrycksTest-high_school_mathematics|5": {
        "acc": 0.37037037037037035,
        "acc_stderr": 0.02944316932303154,
        "acc_norm": 0.37037037037037035,
        "acc_norm_stderr": 0.02944316932303154
    },
    "harness|hendrycksTest-high_school_microeconomics|5": {
        "acc": 0.7142857142857143,
        "acc_stderr": 0.029344572500634332,
        "acc_norm": 0.7142857142857143,
        "acc_norm_stderr": 0.029344572500634332
    },
    "harness|hendrycksTest-high_school_physics|5": {
        "acc": 0.3708609271523179,
        "acc_stderr": 0.03943966699183629,
        "acc_norm": 0.3708609271523179,
        "acc_norm_stderr": 0.03943966699183629
    },
    "harness|hendrycksTest-high_school_psychology|5": {
        "acc": 0.8422018348623853,
        "acc_stderr": 0.01563002297009246,
        "acc_norm": 0.8422018348623853,
        "acc_norm_stderr": 0.01563002297009246
    },
    "harness|hendrycksTest-high_school_statistics|5": {
        "acc": 0.5740740740740741,
        "acc_stderr": 0.03372343271653062,
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    },
    "harness|hendrycksTest-high_school_us_history|5": {
        "acc": 0.8578431372549019,
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        "acc_norm": 0.8578431372549019,
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    },
    "harness|hendrycksTest-high_school_world_history|5": {
        "acc": 0.8565400843881856,
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    },
    "harness|hendrycksTest-human_aging|5": {
        "acc": 0.672645739910314,
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    },
    "harness|hendrycksTest-human_sexuality|5": {
        "acc": 0.7557251908396947,
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    "harness|hendrycksTest-international_law|5": {
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    "harness|hendrycksTest-jurisprudence|5": {
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    },
    "harness|hendrycksTest-logical_fallacies|5": {
        "acc": 0.754601226993865,
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    },
    "harness|hendrycksTest-machine_learning|5": {
        "acc": 0.4732142857142857,
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    },
    "harness|hendrycksTest-management|5": {
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    "harness|hendrycksTest-marketing|5": {
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    },
    "harness|hendrycksTest-medical_genetics|5": {
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    },
    "harness|hendrycksTest-miscellaneous|5": {
        "acc": 0.8084291187739464,
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    "harness|hendrycksTest-moral_disputes|5": {
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    "harness|hendrycksTest-moral_scenarios|5": {
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    },
    "harness|hendrycksTest-nutrition|5": {
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    },
    "harness|hendrycksTest-philosophy|5": {
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    "harness|hendrycksTest-professional_accounting|5": {
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    "harness|hendrycksTest-professional_psychology|5": {
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    },
    "harness|hendrycksTest-public_relations|5": {
        "acc": 0.6818181818181818,
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    },
    "harness|hendrycksTest-security_studies|5": {
        "acc": 0.746938775510204,
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        "acc_norm": 0.746938775510204,
        "acc_norm_stderr": 0.027833023871399677
    },
    "harness|hendrycksTest-sociology|5": {
        "acc": 0.8258706467661692,
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        "acc_norm_stderr": 0.026814951200421603
    },
    "harness|hendrycksTest-us_foreign_policy|5": {
        "acc": 0.91,
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        "acc_norm": 0.91,
        "acc_norm_stderr": 0.028762349126466125
    },
    "harness|hendrycksTest-virology|5": {
        "acc": 0.5783132530120482,
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        "acc_norm_stderr": 0.038444531817709175
    },
    "harness|hendrycksTest-world_religions|5": {
        "acc": 0.7777777777777778,
        "acc_stderr": 0.03188578017686398,
        "acc_norm": 0.7777777777777778,
        "acc_norm_stderr": 0.03188578017686398
    },
    "harness|truthfulqa:mc|0": {
        "mc1": 0.5691554467564259,
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        "mc2": 0.7184177934834866,
        "mc2_stderr": 0.014995634120330182
    },
    "harness|winogrande|5": {
        "acc": 0.8342541436464088,
        "acc_stderr": 0.010450899545370632
    },
    "harness|gsm8k|5": {
        "acc": 0.6535253980288097,
        "acc_stderr": 0.013107179054313398
    }
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 74.4   |
| ARC (25-shot)         | 71.08          |
| HellaSwag (10-shot)   | 88.45    |
| MMLU (5-shot)         | 66.26         |
| TruthfulQA (0-shot)   | 71.84   |
| Winogrande (5-shot)   | 83.43   |
| GSM8K (5-shot)        | 65.35        |