Instructions to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF",
	filename="Phi-4-Open-R1-Distill-EZOv1-15B-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with llama.cpp:

Install from brew

brew 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

Install 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with Ollama:
```
ollama run hf.co/grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF to start chatting

Docker Model Runner
How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with Docker Model Runner:
```
docker model run hf.co/grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M
```

Lemonade

How to use grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grapevine-AI/phi-4-open-R1-Distill-EZOv1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.phi-4-open-R1-Distill-EZOv1-GGUF-Q4_K_M

List all available models

lemonade list

grapevine-AI commited on Feb 24, 2025

Commit

4a21509

verified ·

1 Parent(s): d645b3a

Update README.md

Browse files

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README.md +26 -3

README.md CHANGED Viewed

@@ -1,3 +1,26 @@
----
-license: mit
----

+---
+license: mit
+---
+# 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](https://huggingface.co/AXCXEPT/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](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)データセットを使用しました。<br>
+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