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

Libraries

How to use microsoft/Phi-tiny-MoE-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-tiny-MoE-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-tiny-MoE-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-tiny-MoE-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use microsoft/Phi-tiny-MoE-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/microsoft/Phi-tiny-MoE-instruct

SGLang

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

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

saneowl commited on Jul 13, 2025

Commit

422100f

verified ·

1 Parent(s): 0c3e9df

Fix minor error

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Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -106,7 +106,7 @@ To understand the capabilities, we compare Phi-tiny-MoE with a set of models ove
 | Gemma 3 1B           | 1B            | 1B           | 40.80 | 14.70    | 34.80 | 37.46         | 41.50       | 41.77  | 6.67     |
 | LLaMA 3.2 1B         | 1B            | 1B           | 46.30 | 18.67    | 35.18 | 49.91         | 35.40       | 44.96  | 5.23     |
 | **Our (SlimMoE) Models**       |||||||||||
-| Phi-tiny-MoE         | 7.6B          | 2.4B         | 70.68 | 49.68    | 55.27 | 84.91         | 73.80       | 84.89  | 7.59     |
 | Phi-tiny-MoE         | 3.8B          | 1.1B         | 60.83 | 36.34    | 45.58 | 76.37         | 58.50       | 78.47  | 7.05     |

 | Gemma 3 1B           | 1B            | 1B           | 40.80 | 14.70    | 34.80 | 37.46         | 41.50       | 41.77  | 6.67     |
 | LLaMA 3.2 1B         | 1B            | 1B           | 46.30 | 18.67    | 35.18 | 49.91         | 35.40       | 44.96  | 5.23     |
 | **Our (SlimMoE) Models**       |||||||||||
+| Phi-mini-MoE         | 7.6B          | 2.4B         | 70.68 | 49.68    | 55.27 | 84.91         | 73.80       | 84.89  | 7.59     |
 | Phi-tiny-MoE         | 3.8B          | 1.1B         | 60.83 | 36.34    | 45.58 | 76.37         | 58.50       | 78.47  | 7.05     |