Microsoft Phi-4-mini-instruct GGUF
| Property | Value |
|---|---|
| Original Model | Microsoft Phi-4-mini-instruct |
| Quantization Types | Q2 to Q8 variants |
| Size Range | 1.51GB - 4.08GB |
| Source | Original Model Link |
What is microsoft_Phi-4-mini-instruct-GGUF?
This is a comprehensive collection of GGUF quantized versions of Microsoft's Phi-4-mini-instruct model, optimized for various hardware configurations and use cases. These quantizations were created using llama.cpp release b4792 and feature imatrix optimizations for enhanced performance.
Implementation Details
The model comes in multiple quantization formats, from Q8_0 (highest quality) to Q2_K (smallest size). Each variant is optimized for specific use cases, with special attention paid to embedding and output weight handling. The implementation uses a specific prompt format: "<|system|>{system_prompt}<|end|><|user|>{prompt}<|end|><|assistant|>"
- Q8_0 variant (4.08GB): Highest quality quantization
- Q6_K_L variant (3.30GB): Near-perfect quality with Q8_0 for embed/output weights
- Q4_K_M variant (2.49GB): Recommended default for most use cases
- IQ4_XS variant (2.22GB): Efficient compromise between size and performance
Core Capabilities
- Multiple quantization options for different hardware configurations
- Online repacking support for ARM and AVX CPU inference
- Optimized performance on various hardware architectures
- Special handling of embedding and output weights in specific variants
Frequently Asked Questions
Q: What makes this model unique?
This model offers an extensive range of quantization options, allowing users to choose the perfect balance between model size, quality, and performance for their specific hardware setup. The implementation includes modern features like online repacking and specialized weight handling.
Q: What are the recommended use cases?
For most users, the Q4_K_M variant (2.49GB) provides an excellent balance of quality and size. Users with limited RAM should consider Q3_K variants, while those prioritizing quality should opt for Q6_K_L or Q8_0 variants. The choice depends on your hardware capabilities and quality requirements.
