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Beyonder-4x7B-v3

Maintainer: mlabonne

Total Score

54

Last updated 5/15/2024

👁️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

Beyonder-4x7B-v3 is an improvement over the popular Beyonder-4x7B-v2 model. It is a Mixture of Experts (MoE) model that combines four specialized models using LazyMergekit:

Model Inputs and Outputs

The Beyonder-4x7B-v3 model uses a context window of 8k tokens. It is designed to work well with the Mistral Instruct chat template, which is compatible with LM Studio.

Inputs

  • Text prompts for a variety of tasks, including chat, code generation, role-playing, and math problems.

Outputs

  • Responses generated by the model, which can include:
    • Coherent and contextual conversations
    • Code snippets for various programming languages
    • Detailed role-playing narratives
    • Solutions to mathematical problems

Capabilities

The Beyonder-4x7B-v3 model is a well-rounded AI assistant capable of handling a diverse range of tasks. By combining four specialized experts, the model can leverage different capabilities to provide high-quality responses.

For example, the model can engage in natural conversations while also demonstrating strong coding and problem-solving abilities. The role-playing expert allows the model to create immersive narrative experiences.

What Can I Use It For?

The Beyonder-4x7B-v3 model can be used for a variety of applications, including:

  • Conversational AI assistants: The model's strong conversational abilities make it suitable for building chatbots and virtual assistants.
  • Content creation: The model's versatility allows it to assist with tasks like creative writing, scriptwriting, and story generation.
  • Educational tools: The model's problem-solving and explanatory skills can be leveraged to create interactive learning experiences.
  • Programming assistance: The model's coding capabilities can help developers with tasks like code generation, debugging, and algorithm design.

Things to Try

One interesting aspect of the Beyonder-4x7B-v3 model is its use of a Mixture of Experts (MoE) architecture. This approach allows the model to leverage the strengths of multiple specialized models, leading to improved overall performance.

To get the most out of the model, you can experiment with different inference parameters, such as temperature, top-k, and top-p, to find the settings that work best for your specific use case. Additionally, you can try leveraging the model's versatility by combining its different capabilities, such as using its coding skills to help with a math problem or its storytelling abilities to enhance a conversational experience.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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