README.md

---
language:
- en
license: apache-2.0
tags:
- Math
datasets:
- meta-math/MetaMathQA
pipeline_tag: text-generation
model-index:
- name: Optimus-7B
  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: 65.44
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      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: 85.41
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      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: 63.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      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: 55.79
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      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: 78.77
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      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: 65.5
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Q-bert/Optimus-7B
      name: Open LLM Leaderboard
---

## Optimus-7B

<img src="_c3f4a76b-c0b1-4fba-9537-33f8fd697f2d.jpg" width="300" height="200" alt="Optimus-7B">

Fine-tuned On [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) with [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)

You can use ChatML format.

# [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/results/blob/main/Q-bert/Optimus-7B/results_2023-12-04T18-59-49.207215.json)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 69.09               |
| ARC (25-shot)         | 65.44               |
| HellaSwag (10-shot)   | 85.41               |
| MMLU (5-shot)         | 63.61               |
| TruthfulQA (0-shot)   | 55.79               |
| Winogrande (5-shot)   | 78.77               |
| GSM8K (5-shot)        | 65.50               |


# [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__Optimus-7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.09|
|AI2 Reasoning Challenge (25-Shot)|65.44|
|HellaSwag (10-Shot)              |85.41|
|MMLU (5-Shot)                    |63.61|
|TruthfulQA (0-shot)              |55.79|
|Winogrande (5-shot)              |78.77|
|GSM8k (5-shot)                   |65.50|