Instructions to use microsoft/Phi-3.5-MoE-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3.5-MoE-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3.5-MoE-instruct", 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("microsoft/Phi-3.5-MoE-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-MoE-instruct", 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 microsoft/Phi-3.5-MoE-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Phi-3.5-MoE-instruct"
# 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-3.5-MoE-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3.5-MoE-instruct

SGLang

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

Docker Model Runner
How to use microsoft/Phi-3.5-MoE-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3.5-MoE-instruct
```

nguyenbh commited on Aug 30, 2024

Commit

ae6cb90

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1 Parent(s): a09cede

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@@ -288,6 +288,8 @@ The prompt is the same as the [CLIcK paper](https://arxiv.org/abs/2403.06412) pr
 - GPT-4-turbo: 2024-04-09 version
 - GPT-3.5-turbo: 2023-06-13 version
 | Benchmarks               |   Phi-3.5-MoE-Instruct |   Phi-3.0-Mini-128k-Instruct (June2024) |   Llama-3.1-8B-Instruct |   GPT-4o |   GPT-4o-mini |   GPT-4-turbo |   GPT-3.5-turbo |
 |:-------------------------|-----------------------:|--------------------------------:|------------------------:|---------:|--------------:|--------------:|----------------:|
 | CLIcK                    |                  56.44 |                           29.12 |                   47.82 |    80.46 |         68.5  |         72.82 |           50.98 |
@@ -296,7 +298,7 @@ The prompt is the same as the [CLIcK paper](https://arxiv.org/abs/2403.06412) pr
 | KMMLU (5-shot)           |                  47.92 |                           29.98 |                   20.21 |    64.28 |         51.62 |         59.29 |           42.28 |
 | KMMLU-HARD (0-shot, CoT) |                  25.34 |                           25.68 |                   24.03 |    39.62 |         24.56 |         30.56 |           20.97 |
 | KMMLU-HARD (5-shot)      |                  25.66 |                           25.73 |                   15.81 |    40.94 |         24.63 |         31.12 |           21.19 |
-| Average                  |                  45.82 |                           29.99 |                   29.29 |    62.54 |         50.08 |         56.74 |           39.61 |
 #### CLIcK (Cultural and Linguistic Intelligence in Korean)

 - GPT-4-turbo: 2024-04-09 version
 - GPT-3.5-turbo: 2023-06-13 version
+Overall, the Phi-3.5 MoE model with just 6.6B active params outperforms GPT-3.5-Turbo.
 | Benchmarks               |   Phi-3.5-MoE-Instruct |   Phi-3.0-Mini-128k-Instruct (June2024) |   Llama-3.1-8B-Instruct |   GPT-4o |   GPT-4o-mini |   GPT-4-turbo |   GPT-3.5-turbo |
 |:-------------------------|-----------------------:|--------------------------------:|------------------------:|---------:|--------------:|--------------:|----------------:|
 | CLIcK                    |                  56.44 |                           29.12 |                   47.82 |    80.46 |         68.5  |         72.82 |           50.98 |
 | KMMLU (5-shot)           |                  47.92 |                           29.98 |                   20.21 |    64.28 |         51.62 |         59.29 |           42.28 |
 | KMMLU-HARD (0-shot, CoT) |                  25.34 |                           25.68 |                   24.03 |    39.62 |         24.56 |         30.56 |           20.97 |
 | KMMLU-HARD (5-shot)      |                  25.66 |                           25.73 |                   15.81 |    40.94 |         24.63 |         31.12 |           21.19 |
+| **Average**              |          **45.82** |                **29.99** |            **29.29** |    **62.54** |         **50.08** |         **56.74** |           **39.61** |
 #### CLIcK (Cultural and Linguistic Intelligence in Korean)