---
license: apache-2.0
library_name: transformers
tags:
- merge
pipeline_tag: text-generation
model-index:
- name: TheTop-5x7B-Instruct-S5-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 72.53
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 88.71
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 65.01
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 67.58
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 86.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 70.81
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/TheTop-5x7B-Instruct-S5-v0.1
      name: Open LLM Leaderboard
---

Merge of top 7B models and the SLERP of other 7B models

> mergekit is a toolkit for merging pre-trained language models. mergekit uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

## Eval


![image/png](https://cdn-uploads.huggingface.co/production/uploads/5fd5e18a90b6dc4633f6d292/15qK3CpBMEySfjb0CiT4e.png)

```python
{
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        "acc_stderr": 0.03200912848183244,
        "acc_norm": 0.6553902167958241,
        "acc_norm_stderr": 0.03268788255929441,
        "mc1": 0.5312117503059975,
        "mc1_stderr": 0.01746936487457752,
        "mc2": 0.6758096547963126,
        "mc2_stderr": 0.015381620483561457
    },
    "harness|arc:challenge|25": {
        "acc": 0.6919795221843004,
        "acc_stderr": 0.013491429517292038,
        "acc_norm": 0.7252559726962458,
        "acc_norm_stderr": 0.013044617212771227
    },
    "harness|hellaswag|10": {
        "acc": 0.7234614618601872,
        "acc_stderr": 0.004463721071319078,
        "acc_norm": 0.8870742879904402,
        "acc_norm_stderr": 0.0031585512705264054
    },
    "harness|hendrycksTest-abstract_algebra|5": {
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        "acc_norm": 0.33,
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    },
    "harness|hendrycksTest-anatomy|5": {
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        "acc_norm": 0.6518518518518519,
        "acc_norm_stderr": 0.041153246103369526
    },
    "harness|hendrycksTest-astronomy|5": {
        "acc": 0.7039473684210527,
        "acc_stderr": 0.03715062154998904,
        "acc_norm": 0.7039473684210527,
        "acc_norm_stderr": 0.03715062154998904
    },
    "harness|hendrycksTest-business_ethics|5": {
        "acc": 0.65,
        "acc_stderr": 0.0479372485441102,
        "acc_norm": 0.65,
        "acc_norm_stderr": 0.0479372485441102
    },
    "harness|hendrycksTest-clinical_knowledge|5": {
        "acc": 0.6943396226415094,
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    },
    "harness|hendrycksTest-college_biology|5": {
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    },
    "harness|hendrycksTest-college_chemistry|5": {
        "acc": 0.49,
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    },
    "harness|hendrycksTest-college_computer_science|5": {
        "acc": 0.52,
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        "acc_norm": 0.52,
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    },
    "harness|hendrycksTest-college_mathematics|5": {
        "acc": 0.28,
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    "harness|hendrycksTest-college_medicine|5": {
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    },
    "harness|hendrycksTest-computer_security|5": {
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        "acc_norm": 0.77,
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    "harness|hendrycksTest-conceptual_physics|5": {
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    "harness|hendrycksTest-high_school_macroeconomics|5": {
        "acc": 0.6641025641025641,
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        "acc": 0.3592592592592593,
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    "harness|hendrycksTest-high_school_microeconomics|5": {
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    "harness|hendrycksTest-high_school_physics|5": {
        "acc": 0.36423841059602646,
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    },
    "harness|hendrycksTest-high_school_psychology|5": {
        "acc": 0.8385321100917431,
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    "harness|hendrycksTest-high_school_statistics|5": {
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    "harness|hendrycksTest-high_school_us_history|5": {
        "acc": 0.8529411764705882,
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    "harness|hendrycksTest-high_school_world_history|5": {
        "acc": 0.8143459915611815,
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    "harness|hendrycksTest-human_aging|5": {
        "acc": 0.6816143497757847,
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    "harness|hendrycksTest-human_sexuality|5": {
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    "harness|hendrycksTest-management|5": {
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    "harness|hendrycksTest-marketing|5": {
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    "harness|hendrycksTest-miscellaneous|5": {
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    "harness|hendrycksTest-philosophy|5": {
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    "harness|hendrycksTest-public_relations|5": {
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    "harness|hendrycksTest-security_studies|5": {
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    "harness|hendrycksTest-us_foreign_policy|5": {
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        "acc": 0.8362573099415205,
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    "harness|truthfulqa:mc|0": {
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        "mc2": 0.6758096547963126,
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    "harness|winogrande|5": {
        "acc": 0.861878453038674,
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    },
    "harness|gsm8k|5": {
        "acc": 0.7081122062168309,
        "acc_stderr": 0.012522795894420867
    }
}
```
# [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_MaziyarPanahi__TheTop-5x7B-Instruct-S5-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |75.14|
|AI2 Reasoning Challenge (25-Shot)|72.53|
|HellaSwag (10-Shot)              |88.71|
|MMLU (5-Shot)                    |65.01|
|TruthfulQA (0-shot)              |67.58|
|Winogrande (5-shot)              |86.19|
|GSM8k (5-shot)                   |70.81|