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
- Transformers
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
| {% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|> | |
| ' + message['content'] + '<|end|> | |
| '}}{% elif message['role'] == 'user' %}{{'<|user|> | |
| ' + message['content'] + '<|end|> | |
| '}}{% elif message['role'] == 'assistant' %}{{'<|assistant|> | |
| ' + message['content'] + '<|end|> | |
| '}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> | |
| ' }}{% else %}{{ eos_token }}{% endif %} |