Instructions to use upstage/Solar-Open-100B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/Solar-Open-100B with Transformers:

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

pipe = pipeline("text-generation", model="upstage/Solar-Open-100B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("upstage/Solar-Open-100B")
model = AutoModelForCausalLM.from_pretrained("upstage/Solar-Open-100B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use upstage/Solar-Open-100B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/Solar-Open-100B"
# 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-Open-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/upstage/Solar-Open-100B

SGLang

How to use upstage/Solar-Open-100B 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-Open-100B" \
    --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-Open-100B",
		"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-Open-100B" \
        --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-Open-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use upstage/Solar-Open-100B with Docker Model Runner:
```
docker model run hf.co/upstage/Solar-Open-100B
```

siyoungpark commited on Dec 15, 2025

Commit

7a9b40d

verified ·

1 Parent(s): 473f1f1

Update README: New intro, API date to Dec 31 2025, remove agentic claims

Browse files

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README.md +4 -4

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ tags:
 # **Solar Open**
-**Solar Open** is Upstage's flagship large language model with **102B parameters**, now available under the **Solar-Apache License 2.0** (see [LICENSE](./LICENSE)). It is a **Mixture-of-Experts (MoE)** model designed to empower the open-source community with enterprise-grade reasoning, instruction-following, and agentic capabilities.
 ## Highlights
 * **MoE Architecture (102B / 12B):** Built on a Mixture-of-Experts architecture with **102B total / 12B active parameters**. This design delivers the knowledge depth of a massive model with the inference speed and cost-efficiency of a much smaller model.
@@ -39,9 +39,9 @@ tags:
 *Python code snippets and usage examples will be available upon the official release on December 31, 2025.*
 ## Public API Access
-The official API service for Solar Open is scheduled to launch publicly on **January 1, 2026**.
-* **Access:** Upstage Console (Available starting Jan 1, 2026)
-* **Documentation:** Upstage Console (Available starting Jan 1, 2026)
 ## Citation
 If you use Solar Open in your research, please cite:

 # **Solar Open**
+**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Solar-Apache License 2.0** (see [LICENSE](./LICENSE)). As a **Mixture-of-Experts (MoE)** architecture, it delivers enterprise-grade performance in reasoning, instruction-following, and agentic capabilities—all while prioritizing transparency and customization for the open-source community.
 ## Highlights
 * **MoE Architecture (102B / 12B):** Built on a Mixture-of-Experts architecture with **102B total / 12B active parameters**. This design delivers the knowledge depth of a massive model with the inference speed and cost-efficiency of a much smaller model.
 *Python code snippets and usage examples will be available upon the official release on December 31, 2025.*
 ## Public API Access
+The official API service for Solar Open is scheduled to launch publicly on **December 31, 2025**.
+* **Access:** Upstage Console (Available starting Dec 31, 2025)
+* **Documentation:** Upstage Console (Available starting Dec 31, 2025)
 ## Citation
 If you use Solar Open in your research, please cite: