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

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
```

jeffra commited on Apr 24, 2024

Commit

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1 Parent(s): d32df80

Update README.md

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@@ -8,7 +8,7 @@ tags:
 ## Model Details
-Arctic is a Dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI
 Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of
 Arctic under an Apache-2.0 license. This means you can use them freely in your own research,
 prototypes, and products. Please see our blog
@@ -37,7 +37,7 @@ For the latest details about Snowflake Arctic including tutorials, etc. please r
 Arctic combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B
 total and 17B active parameters chosen using a top-2 gating. For more details about Arctic's model
-architecture please see our cookbook
 ## Usage
@@ -62,4 +62,4 @@ pip install "deepspeed>=0.14.2"
 The Arctic github page has several resources around running inference:
 * Example with pure-HF: https://github.com/Snowflake-Labs/snowflake-arctic/blob/main/inference
-* Tutorial using vLLM: https://github.com/Snowflake-Labs/snowflake-arctic/tree/main/inference/vllm

 ## Model Details
+Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI
 Research Team. We are releasing model checkpoints for both the base and instruct-tuned versions of
 Arctic under an Apache-2.0 license. This means you can use them freely in your own research,
 prototypes, and products. Please see our blog
 Arctic combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B
 total and 17B active parameters chosen using a top-2 gating. For more details about Arctic's model
+Architecture, training process, data, etc. [see our series of cookbooks](https://www.snowflake.com/en/data-cloud/arctic/cookbook/).
 ## Usage
 The Arctic github page has several resources around running inference:
 * Example with pure-HF: https://github.com/Snowflake-Labs/snowflake-arctic/blob/main/inference
+* Tutorial using vLLM: https://github.com/Snowflake-Labs/snowflake-arctic/tree/main/inference/vllm