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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- mistralai/Mistral-7B-v0.3 |
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pipeline_tag: text-classification |
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library_name: transformers |
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tags: |
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- moe |
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- mergekit |
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- MoErges |
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--- |
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Model Name: Marsouuu/MistralBase-4x7B-MoE-ECE-PRYMMAL-Martial - Mixture of Experts (MoE) |
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Description: |
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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. |
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Key Features |
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• 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. |
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• 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. |
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• Storytelling Expert: Designed to assist in creative writing, this expert focuses on generating narratives, constructing dialogues, and offering story-building support for various genres. |
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• 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. |
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Technical Specifications |
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• Model Architecture: Mixture of Experts (MoE) with a gating mechanism that routes inputs to the most relevant expert networks. |
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• Domains: |
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• Mathematics: Advanced reasoning and problem-solving. |
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• Coding: Programming support across multiple languages. |
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• Storytelling: Creative writing and narrative generation. |
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• General Chat: Versatile dialogue handling for various conversational contexts. |
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• Training Data: The model was trained on diverse datasets that cover each expert domain, ensuring robustness and versatility. |
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• Framework: Developed using [Nom du Framework, par exemple: PyTorch, TensorFlow], optimized for the MoE architecture with gated routing. |
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Usage |
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This model can be used for a wide range of applications: |
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• Educational Tools: Assisting with mathematical problems, coding exercises, and creative writing tasks. |
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• Software Development: Providing coding suggestions, code completion, and debugging support. |
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• Creative Writing: Generating stories, dialogues, and narrative content. |
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• Conversational Agents: Implementing chatbots with versatile conversational abilities. |
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Limitations |
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• The model may occasionally generate responses that are not entirely contextually appropriate, especially in cases requiring highly specialized domain knowledge. |
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• Despite its 24-bit precision, it may not perform well with extremely large datasets or tasks that require higher precision levels. |
