Instructions to use llmware/phi-3-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmware/phi-3-ov with Transformers:

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

pipe = pipeline("text-generation", model="llmware/phi-3-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("llmware/phi-3-ov", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("llmware/phi-3-ov", trust_remote_code=True)
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 llmware/phi-3-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmware/phi-3-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": "llmware/phi-3-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmware/phi-3-ov

SGLang

How to use llmware/phi-3-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 "llmware/phi-3-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": "llmware/phi-3-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 "llmware/phi-3-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": "llmware/phi-3-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use llmware/phi-3-ov with Docker Model Runner:
```
docker model run hf.co/llmware/phi-3-ov
```

doberst commited on Oct 1, 2024

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@@ -8,26 +8,24 @@ tags: [green, llmware-chat, p3, ov]
 <!-- Provide a quick summary of what the model is/does. -->
-**phi-3-ov** is an OpenVino int4 quantized version of Microsoft Phi-3-mini-4k-instruct, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
-[**phi-3-mini**](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) is a fact-based question-answering model, optimized for complex business documents.
-Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)
-Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)
 ### Model Description
-- **Developed by:** llmware
 - **Model type:** phi3
-- **Parameters:** 3.8 billion
 - **Model Parent:** microsoft/Phi-3-mini-4k-instruct
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Uses:** Chat, general-purpose LLM
 - **Quantization:** int4
 ## Model Card Contact

 <!-- Provide a quick summary of what the model is/does. -->
+**phi-3-ov** is an OpenVino int4 quantized version of [Microsoft Phi-3-mini-4k-instruct](https://www.huggingface.co/microsoft/Phi-3-mini-4k-instruct), providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
 ### Model Description
+- **Developed by:** microsoft
 - **Model type:** phi3
+- **Parameters:** 3.8 billion
 - **Model Parent:** microsoft/Phi-3-mini-4k-instruct
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
+- **Uses:** Chat, general-purpose LLM
 - **Quantization:** int4
+Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)
+Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)
 ## Model Card Contact