Instructions to use microsoft/Phi-3-medium-128k-instruct-onnx-cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-medium-128k-instruct-onnx-cpu with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-medium-128k-instruct-onnx-cpu", 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-3-medium-128k-instruct-onnx-cpu", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-medium-128k-instruct-onnx-cpu", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-3-medium-128k-instruct-onnx-cpu with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3-medium-128k-instruct-onnx-cpu"
# 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-3-medium-128k-instruct-onnx-cpu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu

SGLang

How to use microsoft/Phi-3-medium-128k-instruct-onnx-cpu 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-3-medium-128k-instruct-onnx-cpu" \
    --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-3-medium-128k-instruct-onnx-cpu",
		"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-3-medium-128k-instruct-onnx-cpu" \
        --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-3-medium-128k-instruct-onnx-cpu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-medium-128k-instruct-onnx-cpu with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu
```

kvaishnavi commited on Jan 23

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@@ -39,7 +39,7 @@ How do you know which is the best ONNX model for you:
 		- No → Access the Hugging Face ONNX models for CPU devices and instructions at [Phi-3-medium-128k-instruct-onnx-cpu](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu).
 ## How to Get Started with the Model
-To support the Phi-3 models across a range of devices, platforms, and EP backends, we introduce a new API to wrap several aspects of generative AI inferencing. This API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps [here](http://aka.ms/generate-tutorial). You can also test this with a [chat app](https://github.com/microsoft/onnxruntime-genai/tree/main/examples/chat_app).
 ## Hardware Supported

 		- No → Access the Hugging Face ONNX models for CPU devices and instructions at [Phi-3-medium-128k-instruct-onnx-cpu](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu).
 ## How to Get Started with the Model
+To support the Phi-3 models across a range of devices, platforms, and EP backends, we introduce a new API to wrap several aspects of generative AI inferencing. This API makes it easy to drag and drop LLMs straight into your app. To run the early version of these models with ONNX, follow the steps [here](http://aka.ms/generate-tutorial).
 ## Hardware Supported