---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- moe
- mergekit
- MoErges
base_model:
- mistralai/Mistral-7B-v0.3
pipeline_tag: text-classification
model-index:
- name: MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 16.97
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 8.87
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.3
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 1.23
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 7.85
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 4.21
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial
      name: Open LLM Leaderboard
---
Model Name: Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial - Mixture of Experts (MoE)

Description:

This is a cutting-edge Mixture of Experts (MoE) model designed with 24-bit precision, tailored to excel in four key domains: mathematics, coding, storytelling, and general chat. Built with a dynamic mixture of expert layers, this model adapts to different tasks by routing inputs to the most relevant expert network, delivering high-quality outputs efficiently.

Key Features

	•	Mathematics Expert: Equipped with specialized mathematical reasoning capabilities, this model is fine-tuned for solving complex mathematical problems, numerical computations, and providing detailed explanations for mathematical concepts.
	•	Coding Expert: The model has been trained extensively on various programming languages and software development paradigms. It can help generate, debug, and explain code snippets, offering a comprehensive coding support experience.
	•	Storytelling Expert: Designed to assist in creative writing, this expert focuses on generating narratives, constructing dialogues, and offering story-building support for various genres.
	•	General Chat Expert: Capable of engaging in everyday conversations, offering accurate and contextually appropriate responses. This expert is versatile and adaptive to different conversational tones, whether it’s casual chit-chat or formal assistance.

Technical Specifications

	•	Model Architecture: Mixture of Experts (MoE) with a gating mechanism that routes inputs to the most relevant expert networks.
	•	Domains:
	•	Mathematics: Advanced reasoning and problem-solving.
	•	Coding: Programming support across multiple languages.
	•	Storytelling: Creative writing and narrative generation.
	•	General Chat: Versatile dialogue handling for various conversational contexts.
	•	Training Data: The model was trained on diverse datasets that cover each expert domain, ensuring robustness and versatility.
	•	Framework: Developed using [Nom du Framework, par exemple: PyTorch, TensorFlow], optimized for the MoE architecture with gated routing.

Usage

This model can be used for a wide range of applications:

	•	Educational Tools: Assisting with mathematical problems, coding exercises, and creative writing tasks.
	•	Software Development: Providing coding suggestions, code completion, and debugging support.
	•	Creative Writing: Generating stories, dialogues, and narrative content.
	•	Conversational Agents: Implementing chatbots with versatile conversational abilities.

Limitations

	•	The model may occasionally generate responses that are not entirely contextually appropriate, especially in cases requiring highly specialized domain knowledge.
	•	Despite its 24-bit precision, it may not perform well with extremely large datasets or tasks that require higher precision levels.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Marsouuu__MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 6.57|
|IFEval (0-Shot)    |16.97|
|BBH (3-Shot)       | 8.87|
|MATH Lvl 5 (4-Shot)| 0.30|
|GPQA (0-shot)      | 1.23|
|MuSR (0-shot)      | 7.85|
|MMLU-PRO (5-shot)  | 4.21|