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

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
```

vllm support

#15

by yaronr - opened Sep 25, 2024

Discussion

yaronr

Sep 25, 2024

•

edited Sep 25, 2024

Hi
Can you please share whether you plan on adding support for your model in vllm? ('SolarForCausalLM' architecture)
We would love to run our independent analysis on solar and share our results (and we use vllm).
Thank you!

yaronr changed discussion title from Chunked prefill & prefix caching to vllm support Sep 25, 2024

Namjun

Oct 2, 2024

Hi @yaronr , vLLM now supports SolarForCausalLM architecture as of v0.6.2. This version was released last week. Please check it out!
https://github.com/vllm-project/vllm/releases/tag/v0.6.2

yaronr

Oct 7, 2024

Thank you. I will give it a try.

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