Cybertron v4 UNA-MGS
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Qwen |
| Base Model | Qwen2.5 7B |
| Training Dataset | Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1 |
What is cybertron-v4-qw7B-UNAMGS-GGUF?
Cybertron v4 UNA-MGS is an advanced language model that combines Qwen2.5's architecture with novel optimization techniques including UNA (Uniform Neural Alignment) and MGS enhancement. The model has achieved significant performance metrics, scoring #1 among 7-8B LLMs with no contamination, achieving an average score of 31.82.
Implementation Details
The model implements specialized optimization at MLP layers using UNA methodology, similar to the approach used in miniclaus-1.5B. It's available in multiple GGUF quantizations, ranging from full F16 weights (15.24GB) to highly compressed versions (2.78GB), offering flexibility for different hardware configurations.
- Implements novel MGS enhancement technique
- Features UNA optimization at MLP layers
- Available in 23 different quantization formats
- Supports various inference backends including CPU, GPU, and Metal
Core Capabilities
- Strong performance on IFEval with 60.84% strict accuracy
- 37.71% normalized accuracy on BBH (3-Shot)
- 29.91% exact match on MATH Lvl 5 (4-Shot)
- 38.89% accuracy on MMLU-PRO (5-shot)
Frequently Asked Questions
Q: What makes this model unique?
The model's unique combination of UNA and MGS optimization techniques, along with its strong performance metrics while maintaining efficiency through various quantization options, sets it apart from other models in its size range.
Q: What are the recommended use cases?
The model excels in conversational AI applications and can be used for various text generation tasks. For optimal performance, users should choose the appropriate quantization based on their hardware capabilities, with Q6_K_L being recommended for high quality and Q4_K_M for balanced performance.
