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---
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.3
pipeline_tag: text-classification
library_name: transformers
tags:
- moe
- mergekit
- MoErges
---
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. |