Instructions to use microsoft/Phi-3-mini-4k-instruct-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-mini-4k-instruct-onnx with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct-onnx", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("microsoft/Phi-3-mini-4k-instruct-onnx", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-3-mini-4k-instruct-onnx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3-mini-4k-instruct-onnx"
# 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-mini-4k-instruct-onnx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-mini-4k-instruct-onnx

SGLang

How to use microsoft/Phi-3-mini-4k-instruct-onnx 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-mini-4k-instruct-onnx" \
    --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-mini-4k-instruct-onnx",
		"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-mini-4k-instruct-onnx" \
        --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-mini-4k-instruct-onnx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-mini-4k-instruct-onnx with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-mini-4k-instruct-onnx
```

Are there tensorflow lite versions of the Phi3 models?

by Zhubarb - opened May 14, 2024

Discussion

Zhubarb

May 14, 2024

I understand there are onnx and gguf versions of the model. I was wondering whether tensorflow lite is also supported?

kvaishnavi

Microsoft org May 21, 2024

TensorFlow Lite (TFLite) is not guaranteed to work with these uploaded ONNX models. These models are optimized for ONNX Runtime (ORT) and converting these ONNX models to TFLite may fail due to the ORT-specific operators in the model.

You can try these steps to convert the uploaded ONNX models to TFLite. If those steps fail, you can try exporting the original PyTorch model to ONNX and convert that ONNX model to TFLite. However, the resulting ONNX model will not be optimized.

kvaishnavi changed discussion status to closed May 21, 2024

henrywang0314

Jun 5, 2024

I'm also interesting about tflite phi3 model.
Did you try the solution or it just not working?

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