How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Use Docker
docker model run hf.co/grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Quick Links
What is this?
AXCXEPT社がphi-4にopen-R1の技術を導入して作成した思考モデル、phi-4-open-R1-Distill-EZOv1をGGUFフォーマットに変換したものです。
imatrix dataset
日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。
また、CUDA版llama.cppがbfloat16に対応したため、imatrixの算出は本来の数値精度であるBF16のモデルを使用して行いました。
Chat template
<|im_start|>system<|im_sep|>ここにSystem Promptを書きます<|im_end|><|im_start|>user<|im_sep|>ここにMessageを書きます<|im_end|><|im_start|>assistant<|im_sep|>
Note
llama.cpp-b4451以降と合わせてご利用ください。
Environment
Windows版llama.cpp-b4514およびllama.cpp-b4524同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。
License
MIT
Developer
Microsoft Research & AXCXEPT
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Hardware compatibility
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:# Run inference directly in the terminal: llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF: