Instructions to use microsoft/Phi-4-mini-flash-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-4-mini-flash-reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/Phi-4-mini-flash-reasoning", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-mini-flash-reasoning", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-4-mini-flash-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-4-mini-flash-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-4-mini-flash-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-4-mini-flash-reasoning

SGLang

How to use microsoft/Phi-4-mini-flash-reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/Phi-4-mini-flash-reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-4-mini-flash-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/Phi-4-mini-flash-reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Phi-4-mini-flash-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-4-mini-flash-reasoning with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-4-mini-flash-reasoning
```

renll commited on Jul 8, 2025

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@@ -20,14 +20,14 @@ widget:
 Phi-4-mini-flash-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities.
 The model belongs to the Phi-4 model family and supports 64K token context length.
-📰 [Phi-4-mini-flash-reasoning Blog](https://aka.ms/phi4-mini-flash-reasoning/blog), and [Developer Article](https://techcommunity.microsoft.com/blog/azuredevcommunityblog/make-phi4-mini-flash-reasoning-more-powerful-with-industry-reasoning-on-edge-devices/4409764)<br>
-📖 [Phi-4-mini-flash-reasoning Paper](https://aka.ms/phi4-mini-flash-reasoning/techreport) | [HF paper](https://huggingface.co/papers/2504.21233) <br>
 👩‍🍳 [Phi Cookbook](https://github.com/microsoft/PhiCookBook) <br>
 🏡 [Phi Portal](https://azure.microsoft.com/en-us/products/phi) <br>
-🖥️ Try It [Azure](https://aka.ms/phi4-mini-flash-reasoning/azure) <br>
-🎉**Phi-4 models**: [[Phi-4-reasoning](https://huggingface.co/microsoft/Phi-4-reasoning)] | [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)];
 [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]
 ## Intended Uses

 Phi-4-mini-flash-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities.
 The model belongs to the Phi-4 model family and supports 64K token context length.
+📰 [Phi-4-mini-flash-reasoning Blog](https://aka.ms/flashreasoning) <br>
+📖 [Phi-4-mini-flash-reasoning Paper](https://aka.ms/flashreasoning-paper) <br>
 👩‍🍳 [Phi Cookbook](https://github.com/microsoft/PhiCookBook) <br>
 🏡 [Phi Portal](https://azure.microsoft.com/en-us/products/phi) <br>
+🖥️ Try It [Azure](https://ai.azure.com/explore/models/Phi-4-mini-flash-reasoning/version/1/registry/azureml-phi-prod) <br>
+🎉**Phi-4 models**: [[Phi-4-mini-reasoning](https://huggingface.co/microsoft/Phi-4-mini-reasoning)] | [[Phi-4-reasoning](https://huggingface.co/microsoft/Phi-4-reasoning)] | [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)];
 [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]
 ## Intended Uses