---
language:
- en
license: apache-2.0
tags:
- Math
datasets:
- meta-math/MetaMathQA
pipeline_tag: text-generation
base_model:
- fblgit/una-cybertron-7b-v2-bf16
- meta-math/MetaMath-Mistral-7B
model-index:
- name: MetaMath-Cybertron
  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: 66.47
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      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: 85.54
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      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: 63.71
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      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: 57.71
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      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: 79.64
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      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: 70.51
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/MetaMath-Cybertron
      name: Open LLM Leaderboard
---

## MetaMath-Cybertron


Merge [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16) and [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) using slerp merge.

You can use ChatML format.

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [Coming soon]()

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | Coming soon               |
| ARC (25-shot)         | Coming soon               |
| HellaSwag (10-shot)   | Coming soon               |
| MMLU (5-shot)         | Coming soon               |
| TruthfulQA (0-shot)   | Coming soon               |
| Winogrande (5-shot)   | Coming soon               |
| GSM8K (5-shot)        | Coming soon               |


# [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_Q-bert__MetaMath-Cybertron)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |70.60|
|AI2 Reasoning Challenge (25-Shot)|66.47|
|HellaSwag (10-Shot)              |85.54|
|MMLU (5-Shot)                    |63.71|
|TruthfulQA (0-shot)              |57.71|
|Winogrande (5-shot)              |79.64|
|GSM8k (5-shot)                   |70.51|