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
```

spacemanidol

adevaraj commited on Apr 24, 2024

Commit

3cc5dd0

verified ·

1 Parent(s): e8bd8ca

respectively (#1)

Browse files

- Set BOS and EOS to <|im_start|> and <|im_end|> respectively (2ede38ff39509acb6f95f3efc75add0547e13838)

Co-authored-by: Ashwin Devaraj <adevaraj@users.noreply.huggingface.co>

Files changed (1) hide show

config.json +3 -3

config.json CHANGED Viewed

@@ -3,7 +3,7 @@
     "ArcticForCausalLM"
   ],
   "attention_dropout": 0,
-  "bos_token_id": 1,
   "enable_expert_tensor_parallelism": false,
   "enc_index": [
     0,
@@ -42,7 +42,7 @@
     33,
     34
   ],
-  "eos_token_id": 2,
   "hidden_act": "silu",
   "hidden_size": 7168,
   "initializer_range": 0.02,
@@ -72,4 +72,4 @@
   "use_cache": true,
   "use_residual": true,
   "vocab_size": 32000
-}

     "ArcticForCausalLM"
   ],
   "attention_dropout": 0,
+  "bos_token_id": 31998,
   "enable_expert_tensor_parallelism": false,
   "enc_index": [
     0,
     33,
     34
   ],
+  "eos_token_id": 31999,
   "hidden_act": "silu",
   "hidden_size": 7168,
   "initializer_range": 0.02,
   "use_cache": true,
   "use_residual": true,
   "vocab_size": 32000
+}