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

Libraries

How to use unsloth/phi-4 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/phi-4")
model = AutoModelForCausalLM.from_pretrained("unsloth/phi-4")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use unsloth/phi-4 with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

Unsloth Studio new

How to use unsloth/phi-4 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 unsloth/phi-4 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 unsloth/phi-4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/phi-4 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/phi-4",
    max_seq_length=2048,
)

Docker Model Runner
How to use unsloth/phi-4 with Docker Model Runner:
```
docker model run hf.co/unsloth/phi-4
```

About the phi-4 training Hyperparameters

by zj2023vegetable - opened Feb 8, 2025

Discussion

zj2023vegetable

Feb 8, 2025

Could you provide some details about the pre-training and post-training(SFT & RL) ?

shimmyshimmer

Unsloth AI org Feb 8, 2025

Could you provide some details about the pre-training and post-training(SFT & RL) ?

You'll need to go to our docs which will give some info. https://docs.unsloth.ai/

Generally the one we set in the notebooks are the best

zj2023vegetable

Feb 8, 2025

•

edited Feb 8, 2025

First of all, thank you for your response! I noticed that in your blog (https://unsloth.ai/blog/phi4), you mentioned fixing a fine-tuning bug, so it seems that you have conducted comprehensive SFT post-training on phi-4. In the Colab, there wasn't a detailed introduction on how many GPUs were used during SFT, i.e., it's impossible to infer the global batch size or the number of tokens used in one update step. Additionally, I noticed that the code in the Colab uses LoRA. Is the currently released phi-4 also fine-tuned using LoRA? Could you kindly provide this detail if it's convenient for you?

shimmyshimmer

Unsloth AI org Feb 13, 2025

First of all, thank you for your response! I noticed that in your blog (https://unsloth.ai/blog/phi4), you mentioned fixing a fine-tuning bug, so it seems that you have conducted comprehensive SFT post-training on phi-4. In the Colab, there wasn't a detailed introduction on how many GPUs were used during SFT, i.e., it's impossible to infer the global batch size or the number of tokens used in one update step. Additionally, I noticed that the code in the Colab uses LoRA. Is the currently released phi-4 also fine-tuned using LoRA? Could you kindly provide this detail if it's convenient for you?

The current phi-4 you see here was not fine-tuned. We just fixed some errors with tokenizers/chat template etc

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