Qwen2.5-14B-YOYO-V4-GGUF
| Property | Value |
|---|---|
| Original Model | YOYO-AI/Qwen2.5-14B-YOYO-V4 |
| Quantization Author | mradermacher |
| Format | GGUF |
| Size Range | 5.9GB - 15.8GB |
What is Qwen2.5-14B-YOYO-V4-GGUF?
This is a quantized version of the Qwen2.5-14B-YOYO-V4 model, offering various compression options to balance between model size, inference speed, and quality. The quantization was performed by mradermacher to create GGUF format files suitable for efficient deployment.
Implementation Details
The model is available in multiple quantization formats, each optimized for different use cases:
- Q2_K: Smallest size at 5.9GB
- Q4_K_S/M: Recommended formats at 8.7GB/9.1GB offering good speed-quality balance
- Q6_K: Very high quality at 12.2GB
- Q8_0: Highest quality at 15.8GB with fast inference
- IQ4_XS: Alternative quantization at 8.3GB
Core Capabilities
- Multiple quantization options for different deployment scenarios
- Fast inference with Q4_K variants
- High-quality preservation with Q6_K and Q8_0 variants
- Compressed model sizes ranging from 5.9GB to 15.8GB
- GGUF format compatibility for easy deployment
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the Qwen2.5-14B-YOYO-V4, allowing users to choose the optimal balance between model size, inference speed, and quality for their specific use case. The GGUF format makes it particularly suitable for efficient deployment.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants (8.7GB/9.1GB) are recommended as they offer a good balance of speed and quality. For highest quality requirements, consider the Q6_K (12.2GB) or Q8_0 (15.8GB) variants. When storage is severely constrained, the Q2_K variant (5.9GB) can be used.
