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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ArchiveStudio/Phi-4-mini-flash-reasoning", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchiveStudio/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 "ArchiveStudio/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": "ArchiveStudio/Phi-4-mini-flash-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use ArchiveStudio/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 "ArchiveStudio/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": "ArchiveStudio/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 "ArchiveStudio/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": "ArchiveStudio/Phi-4-mini-flash-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Phi-4-mini-flash-reasoning / CODE_OF_CONDUCT.md

ArchiveStudio

renll

Duplicate from microsoft/Phi-4-mini-flash-reasoning

abb14fb 1 day ago

preview code

Raw History Blame Contribute Delete

443 Bytes

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct.

Resources:

Microsoft Open Source Code of Conduct
Microsoft Code of Conduct FAQ
Contact opencode@microsoft.com with questions or concerns