Instructions to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu with Transformers:

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

pipe = pipeline("text-generation", model="amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu

SGLang

How to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu 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 "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu" \
    --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": "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu",
		"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 "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu" \
        --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": "amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu with Docker Model Runner:
```
docker model run hf.co/amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu
```

Lemonade

How to use amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amd/Phi-3.5-mini-instruct-onnx-ryzenai-npu

Run and chat with the model (requires XDNA 2 NPU)

lemonade run user.Phi-3.5-mini-instruct-onnx-ryzenai-npu

List all available models

lemonade list

Phi-3.5-mini-instruct-onnx-ryzenai-npu / README.md

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	---
	license: mit
	language:
	- multilingual
	base_model:
	- microsoft/Phi-3.5-mini-instruct
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- nlp
	- code
	- onnx
	- amd
	- ryzenai-npu
	---

	# Phi-3.5-mini-instruct-onnx-ryzenai-npu
	- ## Introduction
	This model was created using Quark Quantization, followed by OGA Model Builder, and finalized with post-processing for NPU deployment.
	- ## Quantization Strategy
	- AWQ / Group 128 / Asymmetric / BFP16 activations / UINT4 Weights
	- ## Quick Start
	For quickstart, refer to [Ryzen AI documentation](https://ryzenai.docs.amd.com/en/latest/npu_oga.html)

	## Evaluation scores
	- The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 6.8701.

	- The average MMLU scores are astronomy - 75, philosophy - 69.77 and management - 79.61.

	#### License
	Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.

	MIT License

	Copyright (c) 2024 Advanced Micro Devices, Inc

	Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
	The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

	THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	SOFTWARE.

	license: MIT license