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
```

Error downloading model

#13

by pr12431 - opened Jul 12, 2024

Discussion

pr12431

Jul 12, 2024

ValueError: Unrecognized configuration class <class 'transformers_modules.microsoft.Phi-3-mini-4k-instruct-onnx.a484edc37e8f7b425a5aefa25e35905769963681.configuration_phi3.Phi3Config'> to build an AutoTokenizer.

How can I solve for this? I just tried the out of the box download code:
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct-onnx", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct-onnx", trust_remote_code=True)

kvaishnavi

Microsoft org Jul 12, 2024

The AutoModelForCausalLM class is for PyTorch models only and not ONNX models. You can use Hugging Face's Optimum to load the ONNX models with the same Hugging Face APIs that you're used to. For the tokenizer, instead of specifying microsoft/Phi-3-mini-128k-instruct-onnx in the AutoTokenizer.from_pretrained method, you need to download the files locally and specify the path to one of the sub-folders within the microsoft/Phi-3-mini-128k-instruct-onnx repo. Here is an example of how to specify the path.

pr12431 changed discussion status to closed Jul 12, 2024

pr12431 changed discussion status to open Jul 12, 2024

kvaishnavi changed discussion status to closed Jul 17, 2024

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