Beyonder-4x7B-v2
| Property | Value |
|---|---|
| Parameter Count | 24.2B parameters |
| Model Type | Mixture of Experts (MoE) |
| Context Length | 8,000 tokens |
| License | Microsoft Research License |
| Format | BF16 |
What is Beyonder-4x7B-v2?
Beyonder-4x7B-v2 is an advanced Mixture of Experts (MoE) model created using mergekit, combining four specialized 7B parameter models into a powerful unified system. The model achieves performance competitive with Mixtral-8x7B-Instruct-v0.1 while using only half the number of experts, demonstrating remarkable efficiency in its architecture.
Implementation Details
The model integrates four base models as experts: OpenChat 3.5-1210 for general conversation, CodeNinja for programming tasks, PiVoT for creative writing, and WizardMath for mathematical reasoning. Each expert is selectively activated based on the input context, ensuring optimal performance for specific tasks.
- Architecture: 4-expert MoE system with selective activation
- Base Model: Marcoro14-7B-slerp
- Quantization Options: Available in GGUF, AWQ, GPTQ, and EXL2 formats
- Evaluation Performance: Achieves 68.77% on ARC-Challenge, 86.8% on HellaSwag, and 65.1% on MMLU
Core Capabilities
- General conversational abilities with strong performance on dialogue tasks
- Advanced code generation and programming assistance
- Creative writing and storytelling capabilities
- Mathematical problem-solving and logical reasoning
- High truthfulness scores (60.68% on TruthfulQA)
Frequently Asked Questions
Q: What makes this model unique?
The model's innovative approach combines four specialized experts into a single system, achieving performance comparable to larger models while maintaining efficiency. It's particularly notable for matching the capabilities of Mixtral-8x7B-Instruct-v0.1 with only half the number of experts.
Q: What are the recommended use cases?
The model excels in diverse applications including general conversation, programming assistance, creative writing, and mathematical problem-solving. With its 8k context window, it's suitable for both short interactions and longer, more complex tasks requiring extended context understanding.
