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tensorblock/Phi-4-mini-instruct-GGUF

🤗 Hugging Face sourcetext-generationmit31 GBGGUF✓ 2 checksumsupdated today
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curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo tensorblock/Phi-4-mini-instruct-GGUF ./model-folder
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microsoft/Phi-4-mini-instruct - GGUF

This repo contains GGUF format model files for microsoft/Phi-4-mini-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4823.

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## Prompt template
<|system|>{system_prompt}<|end|><|user|>{prompt}<|end|><|assistant|>

Model file specification

Filename Quant type File Size Description
Phi-4-mini-instruct-Q2_K.gguf Q2_K 1.683 GB smallest, significant quality loss - not recommended for most purposes
Phi-4-mini-instruct-Q3_K_S.gguf Q3_K_S 1.897 GB very small, high quality loss
Phi-4-mini-instruct-Q3_K_M.gguf Q3_K_M 2.118 GB very small, high quality loss
Phi-4-mini-instruct-Q3_K_L.gguf Q3_K_L 2.250 GB small, substantial quality loss
Phi-4-mini-instruct-Q4_0.gguf Q4_0 2.325 GB legacy; small, very high quality loss - prefer using Q3_K_M
Phi-4-mini-instruct-Q4_K_S.gguf Q4_K_S 2.338 GB small, greater quality loss
Phi-4-mini-instruct-Q4_K_M.gguf Q4_K_M 2.492 GB medium, balanced quality - recommended
Phi-4-mini-instruct-Q5_0.gguf Q5_0 2.728 GB legacy; medium, balanced quality - prefer using Q4_K_M
Phi-4-mini-instruct-Q5_K_S.gguf Q5_K_S 2.728 GB large, low quality loss - recommended
Phi-4-mini-instruct-Q5_K_M.gguf Q5_K_M 2.848 GB large, very low quality loss - recommended
Phi-4-mini-instruct-Q6_K.gguf Q6_K 3.156 GB very large, extremely low quality loss
Phi-4-mini-instruct-Q8_0.gguf Q8_0 4.085 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Phi-4-mini-instruct-GGUF --include "Phi-4-mini-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Phi-4-mini-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'