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
```

Appreciate the model drop!

by Nitral-AI - opened Sep 11, 2024

Discussion

Nitral-AI

Sep 11, 2024

But why is it only 4k? Its 2024 man, those are rookie numbers.

hunkim

upstage org Sep 11, 2024

Thank you! As this is a preview, it comes with some limitations. We’re committed to making improvements based on user feedback. Once again, we really appreciate your input!

smcleod

Sep 11, 2024

Can’t wait to see what it can do when it can work with at least 64k context!

SaisExperiments

Sep 13, 2024

But why is it only 4k? Its 2024 man, those are rookie numbers.

https://huggingface.co/upstage/solar-pro-preview-instruct/discussions/3
Seems we'll never get the extended context

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