Instructions to use microsoft/Phi-3-small-8k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Phi-3-small-8k-instruct with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/Phi-3-small-8k-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-small-8k-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Phi-3-small-8k-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-small-8k-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-small-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/Phi-3-small-8k-instruct

SGLang

How to use microsoft/Phi-3-small-8k-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-small-8k-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-small-8k-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-small-8k-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-small-8k-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/Phi-3-small-8k-instruct with Docker Model Runner:
```
docker model run hf.co/microsoft/Phi-3-small-8k-instruct
```

Phi-3-small-8k-instruct

Commit History

Dec 10, 2025

20:20
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Jun 12, 2024

16:17
Enabled the AutoModelForSequenceClassification in the auto_map (#22)⁠

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Ensure the query_states and key_states remain in bf16 (#21)⁠

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Jun 3, 2024

21:16
Update tokenization_phi3_small.py (#18)⁠

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May 31, 2024

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Add link to Phi-3 vision ONNX models

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May 29, 2024

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Update README.md

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May 22, 2024

16:48
Add attention_bias to make TGI work (#3)⁠

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May 21, 2024

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May 2, 2024

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Commit History

Dec 10, 2025

Update README.md (#34)⁠

Upload data_summary_card.md (#33)⁠

Aug 30, 2024

Update tokenization_phi3_small.py (#29)⁠

Aug 20, 2024

Update README.md

Jun 12, 2024

Enabled the AutoModelForSequenceClassification in the auto_map (#22)⁠

Ensure the query_states and key_states remain in bf16 (#21)⁠

Jun 3, 2024

Update tokenization_phi3_small.py (#18)⁠

May 31, 2024

Add link to Phi-3 vision ONNX models

May 29, 2024

Update README.md

May 22, 2024

Add attention_bias to make TGI work (#3)⁠

May 21, 2024

Update README

May 2, 2024

chore(root): Initial files upload.⁠

Initial commit