Instructions to use SSON9/solar-open2-tiny-dummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SSON9/solar-open2-tiny-dummy with Transformers:

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

pipe = pipeline("text-generation", model="SSON9/solar-open2-tiny-dummy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SSON9/solar-open2-tiny-dummy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SSON9/solar-open2-tiny-dummy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SSON9/solar-open2-tiny-dummy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SSON9/solar-open2-tiny-dummy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SSON9/solar-open2-tiny-dummy

SGLang

How to use SSON9/solar-open2-tiny-dummy 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 "SSON9/solar-open2-tiny-dummy" \
    --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": "SSON9/solar-open2-tiny-dummy",
		"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 "SSON9/solar-open2-tiny-dummy" \
        --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": "SSON9/solar-open2-tiny-dummy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SSON9/solar-open2-tiny-dummy with Docker Model Runner:
```
docker model run hf.co/SSON9/solar-open2-tiny-dummy
```

solar-open2-tiny-dummy / README.md

SSON9

Add solar-open2 tiny dummy (random weights, CI integration test)

919b792 verified 18 days ago

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	---
	license: apache-2.0
	library_name: transformers
	---

	# solar-open2-tiny-dummy

	Random-weight tiny dummy of the [SolarOpen2](https://huggingface.co/upstage/Solar-Open2-250B) architecture,
	used for `transformers` CI integration tests. Not a trained model — outputs are meaningless.

	Structure mirrors the public model at small scale: hybrid attention with the default `gqa_interval=3`-derived
	pattern (`[full_attention, linear_attention x3] x3`, 12 layers), NoPE full attention with output sigmoid gate,
	factored Kimi-Delta-Attention projections (head_dim 128), and a 16-expert top-4 MoE with one shared expert.
	Tokenizer files are copied from `upstage/Solar-Open2-250B`. Weights are randomly initialized with a fixed seed
	(`torch.manual_seed(42)`) in bfloat16 (617M parameters).