GPAI Ledger › Phi-4 (Microsoft) › Capture 11 Aug 2026

Phi-4 — capture 20260811T105423Z

Verify: sha256sum 11688ac572eda1a895d31a0aa5b037fdeef6476910ee79552c9e39c58940d221.html.txt must equal the hash above (the filename IS the expected hash); ots verify 11688ac572eda1a895d31a0aa5b037fdeef6476910ee79552c9e39c58940d221.html.ots -f 11688ac572eda1a895d31a0aa5b037fdeef6476910ee79552c9e39c58940d221.html.txt (opentimestamps.org) proves the capture time (fresh proofs report 'pending' until bitcoin-anchored, typically within a day).

Extracted text

Machine-extracted text (layout may be lost; the authoritative content is the stored file above).

data_summary_card.md · microsoft/phi-4 at main

Hugging Face

 Models
 Datasets
 Spaces
 Buckets
new
 Docs
 Enterprise

Pricing

Website

Tasks

HuggingChat

Collections

Languages

Organizations

Community

Blog

Posts

Daily Papers

Hardware

Learn

Discord

Forum

GitHub

Solutions

Team & Enterprise

Hugging Face PRO

Enterprise Support

Inference Providers

Inference Endpoints

Storage Buckets

Log In

Sign Up

microsoft

/

phi-4

like

2.29k

Follow

Microsoft

21.5k

Text Generation

Transformers

Safetensors

English

phi3

phi

nlp

math

code

chat

conversational

Eval Results

text-generation-inference

arxiv:

2412.08905

License:

mit

Model card

Files

Files and versions

xet

Community

64

 Deploy

 Copy to bucket
new

 Use this model

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

Libraries

Transformers

How to use microsoft/phi-4 with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/phi-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-4")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-4", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference

Inference Providers

HuggingChat

Notebooks

Google Colab

Kaggle

Local Apps

Settings

vLLM

How to use microsoft/phi-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/phi-4"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker

docker model run hf.co/microsoft/phi-4

SGLang

How to use microsoft/phi-4 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner

How to use microsoft/phi-4 with Docker Model Runner:

docker model run hf.co/microsoft/phi-4

 main

phi-4

/

data_summary_card.md

gugarosa

Upload data_summary_card.md (
#52
)

932b33c

verified

9 months ago

preview

code

|

 Raw

 Download with hf CLI

Copy download link
 History

 Blame

 Contribute

 Delete

Safe

 4.77 kB

		Data Summary for microsoft_phi-4

		1. General information

1.0.1 Version of the Summary:
 1.0

1.0.2 Last update:
 24-Nov-2025

		1.1 Model Developer Identification

1.1.1 Model Developer name and contact details:
 Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080

		1.2 Model Identification

1.2.1 Versioned model name(s):
 phi-4

1.2.2 Model release date:
 12-Dec-2024

		1.3 Overall training data size and characteristics

		1.3.1 Size of dataset and characteristics

1.3.1.A Text training data size:
 1 billion to 10 trillion tokens

1.3.1.B Text training data content:
 Training data is an extension of the data used for Phi-3 and includes a wide variety of sources from:

Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code.

Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).

Acquired academic books and Q&A datasets.

High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.

Multilingual data constitutes about 8% of our overall data.

1.3.1.C Image training data size:
 Not applicable. Images are not part of the training

1.3.1.D Image training data content:
 Not applicable

1.3.1.E Audio training data size:
 Not applicable. Audio data is not part of the training data

1.3.1.F Audio training data content:
 Not applicable

1.3.1.G Video training data size:
 Not applicable. Video data is not part of the training data

1.3.1.H Video training data content:
 Not applicable

1.3.1.I Other training data size:
 Not applicable

1.3.1.J Other training data content:
 Not applicable

1.3.2 Latest date of data acquisition/collection for model training:
 30-Jun-2024

1.3.3 Is data collection ongoing to update the model with new data collection after deployment?
 No

1.3.4 Date the training dataset was first used to train the model:
 10/01/2024

1.3.5 Rationale or purpose of data selection:
 Datasets were selected to maximize high-quality reasoning and problem-solving capabilities. The mixture emphasizes synthetic, curriculum-structured data and rigorously filtered organic sources such as academic papers, licensed books, code, and Q&A to improve STEM reasoning, coding, and general knowledge while reducing noise and contamination. Targeted acquisitions and multilingual content complement synthetic data to balance reasoning strength with factual coverage

		2. List of data sources

		2.1 Publicly available datasets

2.1.1 Have you used publicly available datasets to train the model?
 Yes

		2.2 Private non-publicly available datasets obtained from third parties

		2.2.1 Datasets commercially licensed by rights holders or their representatives

2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?
 Yes

		2.2.2 Private datasets obtained from other third-parties

2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?
 This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)

		2.3 Personal Information

2.3.1 Was personal data used to train the model?
 Microsoft follows all relevant laws and regulations pertaining to personal information

		2.4 Synthetic data

2.4.1 Was any synthetic AI-generated data used to train the model?
 Yes

		3. Data processing aspects

		3.1 Respect of reservation of rights from text and data mining exception or limitation

3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?
 Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent

		3.2 Other information

3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?
 Microsoft follows all required regulations and laws for protecting consumer identities

3.2.2 Was the dataset cleaned or modified before model training?
 Yes