---
language:
- en
license: apache-2.0
datasets:
- HuggingFaceTB/cosmopedia
- EleutherAI/proof-pile-2
- bigcode/the-stack-dedup
- math-ai/AutoMathText
metrics:
- accuracy
- code_eval
model-index:
- name: Mistral_Pro_8B_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: 62.2
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_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: 82.13
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_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: 61.74
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_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: 49.32
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_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: 76.8
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_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: 34.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TencentARC/Mistral_Pro_8B_v0.1
      name: Open LLM Leaderboard
---


# Mistral-Pro-8B Model Card

## Model Description
Mistral-Pro is a progressive version of the original [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.

## Development and Training
Developed by Tencent's ARC Lab, Mistral-Pro is an 8 billion parameter model. It's an expansion of Mistral-7B, further trained on code and math corpora.

## Intended Use
This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages.

## Performance
Mistral_Pro_8B_v0.1 showcases superior performance on a range of benchmarks. It enhances the code and math performance of Mistral. Furthermore, it matches the performance of the recently dominant model, [Gemma](https://huggingface.co/google/gemma-7b).

### Overall Performance on Languages, math and code tasks

  | Model | ARC | Hellaswag | MMLU | TruthfulQA | Winogrande | GSM8K  | HumanEval | 
  | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | 
  | Gemma-7B | 61.9 | 82.2 | 64.6 | 44.8 | 79.0 | 50.9 | 32.3 | 
  | Mistral-7B | 60.8 | 83.3 | 62.7 | 42.6 | 78.0 | 39.2 | 28.7 |
  | Mistral_Pro_8B_v0.1 | 63.2 | 82.6 | 60.6 | 48.3 | 78.9 | 50.6 | 32.9 | 


## Limitations
While Mistral-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.

## Ethical Considerations
Users should be aware of potential biases in the model and use it responsibly, considering its impact on various applications.
# [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_TencentARC__Mistral_Pro_8B_v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |61.06|
|AI2 Reasoning Challenge (25-Shot)|62.20|
|HellaSwag (10-Shot)              |82.13|
|MMLU (5-Shot)                    |61.74|
|TruthfulQA (0-shot)              |49.32|
|Winogrande (5-shot)              |76.80|
|GSM8k (5-shot)                   |34.19|