metadata
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
Detailed results can be found here
| 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 |