Beyonder-4x7B-v3
| Property | Value |
|---|---|
| Parameter Count | 24.2B |
| Model Type | Mixture of Experts (MoE) |
| Context Length | 8,192 tokens |
| License | CC-BY-NC-4.0 |
| Format | FP16 |
What is Beyonder-4x7B-v3?
Beyonder-4x7B-v3 is an advanced Mixture of Experts (MoE) model that combines four specialized 7B parameter models into a powerful 24.2B parameter system. Built using LazyMergekit, it represents an improvement over its predecessor, integrating AlphaMonarch-7B, CodeNinja-1.0-OpenChat-7B, Kunoichi-DPO-v2-7B, and NeuralDaredevil-7B as expert models.
Implementation Details
The model utilizes a sophisticated architecture where each expert specializes in specific tasks: general chat/assistance, code generation, creative writing/roleplay, and mathematical reasoning. During operation, two experts are always engaged in generating responses, enabling cross-pollination of capabilities.
- 8K context window support
- Optimized for Mistral Instruct chat template
- Available in multiple quantized versions (GGUF, ExLlamaV2)
- Demonstrates superior performance on both Nous benchmark suite and EQ-Bench
Core Capabilities
- General conversational AI and assistance
- Code generation and programming tasks
- Creative writing and role-playing scenarios
- Mathematical problem-solving and reasoning
- Hybrid task handling through expert collaboration
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its expert-mixing approach, where it combines specialized capabilities from four different models, allowing for versatile task handling while maintaining high performance across various benchmarks. Its architecture ensures that every response benefits from multiple areas of expertise.
Q: What are the recommended use cases?
The model excels in diverse applications including general chat, code development, creative writing, and mathematical problem-solving. It's particularly effective when tasks require multiple types of expertise, such as explaining technical concepts creatively or combining mathematical reasoning with code implementation.
