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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-4-mini-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-mini-instruct", trust_remote_code=True)
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
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

Suggested tokenizer changes similar to Phi-4

by l2dy - opened Feb 28, 2025

Discussion

l2dy

Feb 28, 2025

tokenizer_config.json for Phi-4-mini-instruct also contains the following,

  "bos_token": "<|endoftext|>",
  "eos_token": "<|endoftext|>",
  "pad_token": "<|endoftext|>",

which was changed in Phi-4 to use different strings. https://huggingface.co/microsoft/phi-4/commit/6fbb3d3bbe726c99b4188087b4deeec1bceac5ae

Does it make sense to apply similar changes to Phi-4-mini-instruct?

Mungert

Mar 2, 2025

•

edited Mar 2, 2025

I have applied the Unsloth fixes when creating the gguf files here : https://huggingface.co/Mungert/Phi-4-mini-instruct.gguf . And it seems to perform better than the original. Not benchmarked its just what I found when testing function calls during development. So I would go ahead and try the fixes on this version.

lunahr

Mar 3, 2025

I have applied the Unsloth fixes when creating the gguf files here : https://huggingface.co/Mungert/Phi-4-mini-instruct.gguf . And it seems to perform better than the original. Not benchmarked its just what I found when testing function calls during development. So I would go ahead and try the fixes on this version.

Note that the pipeline() calls don't work with the fixed tokenizer but I guess it's not a base model so it cannot be used for text completion

Mungert

Mar 3, 2025

I have only tested using llama.cpp and gguf so I don't know about potential pipeline() issues. If you do use Unsloth bug fixes then be sure to use their latest version as this "eos_token": "<|endoftext|>" had been left, incorrect, by mistake in previous version. Should be "eos_token": "<|end|>". Files that have this incorrect : special_tokens_map.json, config.json, generation_config.json and tokenizer_config.json. Or use this as your base https://huggingface.co/unsloth/Phi-4-mini-instruct/tree/main it now has the correct EOS.

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