Instructions to use Snowflake/snowflake-arctic-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Snowflake/snowflake-arctic-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Snowflake/snowflake-arctic-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Snowflake/snowflake-arctic-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Snowflake/snowflake-arctic-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Snowflake/snowflake-arctic-instruct

SGLang

How to use Snowflake/snowflake-arctic-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 "Snowflake/snowflake-arctic-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": "Snowflake/snowflake-arctic-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 "Snowflake/snowflake-arctic-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": "Snowflake/snowflake-arctic-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Snowflake/snowflake-arctic-instruct with Docker Model Runner:
```
docker model run hf.co/Snowflake/snowflake-arctic-instruct
```

Great minds think alike. (Another similar architecture and system co-design)

by withinmiaov - opened Apr 25, 2024

Discussion

withinmiaov

Apr 25, 2024

Amazing work! We also address the All-to-All communication problem by using a similar communication-computation overlap approach, called Shortcut-connected MoE architecture. We are excited to see that such an approach can be applied to real products in the industry. If you are interested, we have more details on this in our paper: "Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Expert" https://arxiv.org/abs/2404.05019

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