Instructions to use OpenVINO/Phi-3-medium-4k-instruct-fp16-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenVINO/Phi-3-medium-4k-instruct-fp16-ov with Transformers:

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

pipe = pipeline("text-generation", model="OpenVINO/Phi-3-medium-4k-instruct-fp16-ov", 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("OpenVINO/Phi-3-medium-4k-instruct-fp16-ov", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OpenVINO/Phi-3-medium-4k-instruct-fp16-ov", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenVINO/Phi-3-medium-4k-instruct-fp16-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenVINO/Phi-3-medium-4k-instruct-fp16-ov

SGLang

How to use OpenVINO/Phi-3-medium-4k-instruct-fp16-ov 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 "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov" \
    --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": "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov",
		"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 "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov" \
        --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": "OpenVINO/Phi-3-medium-4k-instruct-fp16-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenVINO/Phi-3-medium-4k-instruct-fp16-ov with Docker Model Runner:
```
docker model run hf.co/OpenVINO/Phi-3-medium-4k-instruct-fp16-ov
```

Phi-3-medium-4k-instruct-fp16-ov / openvino_detokenizer.xml

katuni4ka

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f0e2eca verified about 2 years ago

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	<?xml version="1.0"?>
	<net name="detokenizer" version="11">
	<layers>
	<layer id="0" name="Parameter_37" type="Parameter" version="opset1">
	<data shape="?,?" element_type="i64" />
	<output>
	<port id="0" precision="I64" names="Parameter_37">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="1" name="Constant_2" type="Const" version="opset1">
	<data element_type="u8" shape="499991" offset="0" size="499991" />
	<output>
	<port id="0" precision="U8">
	<dim>499991</dim>
	</port>
	</output>
	</layer>
	<layer id="2" name="Convert_47" type="Convert" version="opset1">
	<data destination_type="i32" />
	<input>
	<port id="0" precision="I64">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</input>
	<output>
	<port id="1" precision="I32">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="3" name="SentencepieceDetokenizer_38" type="SentencepieceDetokenizer" version="extension">
	<input>
	<port id="0" precision="U8">
	<dim>499991</dim>
	</port>
	<port id="1" precision="I32">
	<dim>-1</dim>
	<dim>-1</dim>
	</port>
	</input>
	<output>
	<port id="2" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="3" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="4" precision="U8">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="4" name="StringTensorPack_39" type="StringTensorPack" version="extension">
	<data mode="begins_ends" />
	<input>
	<port id="0" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="1" precision="I32">
	<dim>-1</dim>
	</port>
	<port id="2" precision="U8">
	<dim>-1</dim>
	</port>
	</input>
	<output>
	<port id="3" precision="STRING" names="string_output">
	<dim>-1</dim>
	</port>
	</output>
	</layer>
	<layer id="5" name="Result_40" type="Result" version="opset1">
	<input>
	<port id="0" precision="STRING">
	<dim>-1</dim>
	</port>
	</input>
	</layer>
	</layers>
	<edges>
	<edge from-layer="0" from-port="0" to-layer="2" to-port="0" />
	<edge from-layer="1" from-port="0" to-layer="3" to-port="0" />
	<edge from-layer="2" from-port="1" to-layer="3" to-port="1" />
	<edge from-layer="3" from-port="2" to-layer="4" to-port="0" />
	<edge from-layer="3" from-port="3" to-layer="4" to-port="1" />
	<edge from-layer="3" from-port="4" to-layer="4" to-port="2" />
	<edge from-layer="4" from-port="3" to-layer="5" to-port="0" />
	</edges>
	<rt_info>
	<bos_token_id value="1" />
	<chat_template value="{% for message in messages %}{% if (message['role'] == 'user') %}{{'<\|user\|>' + ' ' + message['content'] + '<\|end\|>' + ' ' + '<\|assistant\|>' + ' '}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<\|end\|>' + ' '}}{% endif %}{% endfor %}" />
	<eos_token_id value="32000" />
	<original_tokenizer_class value="<class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>" />
	<pad_token_id value="32000" />
	</rt_info>
	</net>