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

Libraries

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

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

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

nguyenbh commited on Aug 22, 2024

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@@ -52,7 +52,7 @@ This is an update over the June 2024 instruction-tuned Phi-3 Mini release based
 The table below highlights multilingual capability of the Phi-3.5 Mini on multilingual MMLU, MEGA, and multilingual MMLU-pro datasets. Overall, we observed that even with just 3.8B active parameters, the model is  competitive on multilingual tasks in comparison to other models with a much bigger active parameters.
-| Benchmark                  | Phi-3.5 Mini-Ins | Phi-3-Mini-128K-Ins (June 2024) | Mistral-7B-Instruct-v0.3 | Mistral-Nemo-12B-Ins-2407 | Llama-3.1-8B-Ins | Gemma-2-9B-Ins | Gemini 1.5 Flash | GPT-4o-mini-2024-07-18 (Chat) |
 |----------------------------|------------------|-----------------------|--------------------------|---------------------------|------------------|----------------|------------------|-------------------------------|
 | Multilingual MMLU          | 55.4             | 51.08                 | 47.4                     | 58.9                      | 56.2             | 63.8           | 77.2             | 72.9                          |
 | Multilingual MMLU-Pro      | 30.9             | 30.21                 | 15.0                     | 34.0                      | 21.4             | 43.0           | 57.9             | 53.2                          |
@@ -66,7 +66,7 @@ The table below highlights multilingual capability of the Phi-3.5 Mini on multil
 The table below shows Multilingual MMLU scores in some of the supported languages. For more multi-lingual benchmarks and details, see [Appendix A](#appendix-a).
-| Benchmark | Phi-3.5 Mini-Ins | Phi-3-Mini-128K-Ins (June 2024) | Mistral-7B-Instruct-v0.3 | Mistral-Nemo-12B-Ins-2407 | Llama-3.1-8B-Ins | Gemma-2-9B-Ins | Gemini 1.5 Flash | GPT-4o-mini-2024-07-18 (Chat) |
 |-----------|------------------|-----------------------|--------------------------|---------------------------|------------------|----------------|------------------|-------------------------------|
 | Arabic    | 44.2             | 35.4                  | 33.7                     | 45.3                      | 49.1             | 56.3           | 73.6             | 67.1                          |
 | Chinese   | 52.6             | 46.9                  | 45.9                     | 58.2                      | 54.4             | 62.7           | 66.7             | 70.8                          |

 The table below highlights multilingual capability of the Phi-3.5 Mini on multilingual MMLU, MEGA, and multilingual MMLU-pro datasets. Overall, we observed that even with just 3.8B active parameters, the model is  competitive on multilingual tasks in comparison to other models with a much bigger active parameters.
+| Benchmark                  | Phi-3.5 Mini-Ins | Phi-3.0-Mini-128k-Instruct (June2024) | Mistral-7B-Instruct-v0.3 | Mistral-Nemo-12B-Ins-2407 | Llama-3.1-8B-Ins | Gemma-2-9B-Ins | Gemini 1.5 Flash | GPT-4o-mini-2024-07-18 (Chat) |
 |----------------------------|------------------|-----------------------|--------------------------|---------------------------|------------------|----------------|------------------|-------------------------------|
 | Multilingual MMLU          | 55.4             | 51.08                 | 47.4                     | 58.9                      | 56.2             | 63.8           | 77.2             | 72.9                          |
 | Multilingual MMLU-Pro      | 30.9             | 30.21                 | 15.0                     | 34.0                      | 21.4             | 43.0           | 57.9             | 53.2                          |
 The table below shows Multilingual MMLU scores in some of the supported languages. For more multi-lingual benchmarks and details, see [Appendix A](#appendix-a).
+| Benchmark | Phi-3.5 Mini-Ins | Phi-3.0-Mini-128k-Instruct (June2024) | Mistral-7B-Instruct-v0.3 | Mistral-Nemo-12B-Ins-2407 | Llama-3.1-8B-Ins | Gemma-2-9B-Ins | Gemini 1.5 Flash | GPT-4o-mini-2024-07-18 (Chat) |
 |-----------|------------------|-----------------------|--------------------------|---------------------------|------------------|----------------|------------------|-------------------------------|
 | Arabic    | 44.2             | 35.4                  | 33.7                     | 45.3                      | 49.1             | 56.3           | 73.6             | 67.1                          |
 | Chinese   | 52.6             | 46.9                  | 45.9                     | 58.2                      | 54.4             | 62.7           | 66.7             | 70.8                          |