Instructions to use upstage/solar-pro-preview-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/solar-pro-preview-instruct with Transformers:

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

pipe = pipeline("text-generation", model="upstage/solar-pro-preview-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("upstage/solar-pro-preview-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use upstage/solar-pro-preview-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/upstage/solar-pro-preview-instruct

SGLang

How to use upstage/solar-pro-preview-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 "upstage/solar-pro-preview-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": "upstage/solar-pro-preview-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 "upstage/solar-pro-preview-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": "upstage/solar-pro-preview-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use upstage/solar-pro-preview-instruct with Docker Model Runner:
```
docker model run hf.co/upstage/solar-pro-preview-instruct
```

Is there any way to use multi gpu?

by lee1111 - opened Sep 12, 2024

Discussion

lee1111

Sep 12, 2024

I am using rtx3090*2 gpus and I need to use both of them to load the model. Is there any way to use multi gpu?

Minjeong

upstage org Sep 14, 2024

Thank you for your question! You can load and use a model with multiple GPUs on a RTX3090 with the code below. 🙂

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("upstage/solar-pro-preview-instruct")
model = AutoModelForCausalLM.from_pretrained(
    "upstage/solar-pro-preview-instruct",
    device_map="auto",  
    torch_dtype="auto",  
    trust_remote_code=True,
)

hunkim

upstage org Sep 14, 2024

@lee1111 Please let us know if this code can address your issue. Thank you!

lee1111

Sep 23, 2024

Thank you so much, the problem has been solved

lee1111 changed discussion status to closed Sep 23, 2024

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