Instructions to use upstage/Solar-Open2-250B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/Solar-Open2-250B with Transformers:

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

pipe = pipeline("text-generation", model="upstage/Solar-Open2-250B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("upstage/Solar-Open2-250B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use upstage/Solar-Open2-250B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/Solar-Open2-250B"
# 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-Open2-250B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/upstage/Solar-Open2-250B

SGLang

How to use upstage/Solar-Open2-250B 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-Open2-250B" \
    --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-Open2-250B",
		"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-Open2-250B" \
        --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-Open2-250B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use upstage/Solar-Open2-250B with Docker Model Runner:
```
docker model run hf.co/upstage/Solar-Open2-250B
```

junyeoplee commited on Jul 31

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@@ -22,7 +22,7 @@ tags:
 Solar Open 2 is Upstage’s 250B-A15B open-weight large language model, built for agentic use cases such as office productivity, document-intensive work, and coding. Its Hybrid-Attention Mixture-of-Experts (MoE) architecture with linear attention delivers highly efficient inference even in long-context settings.
-[**Technical Report**](https://arxiv.org/abs/2607.20062) | [**Blog**](https://www.upstage.ai/blog/en/solar-open-2?utm_source=hf&utm_medium=referral&utm_campaign=so2-launch&utm_content=modelcard) | [**Upstage Website**](https://www.upstage.ai/) | [**Try Demo (~7/31)**](https://open2-beta.upstage.ai/)
 ### Highlights

 Solar Open 2 is Upstage’s 250B-A15B open-weight large language model, built for agentic use cases such as office productivity, document-intensive work, and coding. Its Hybrid-Attention Mixture-of-Experts (MoE) architecture with linear attention delivers highly efficient inference even in long-context settings.
+[**Technical Report**](https://arxiv.org/abs/2607.20062) | [**Blog**](https://www.upstage.ai/blog/en/solar-open-2?utm_source=hf&utm_medium=referral&utm_campaign=so2-launch&utm_content=modelcard) | [**Upstage Website**](https://www.upstage.ai/)
 ### Highlights