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")
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 Jan 16

Commit

b87796b

verified ·

1 Parent(s): c90f8f3

Update README.md from tmp

Browse files

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README.md +5 -5

README.md CHANGED Viewed

@@ -4,7 +4,7 @@ language:
 - ko
 library_name: transformers
 license: other
-license_name: solar-apache-2.0
 pipeline_tag: text-generation
 tags:
 - upstage
@@ -21,7 +21,7 @@ arxiv: 2601.07022
 # **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) for details). 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.
 [**Technical Report**](https://huggingface.co/papers/2601.07022) | [**Project Page**](https://upstage.ai)
@@ -41,7 +41,7 @@ arxiv: 2601.07022
 * **Pre-training Tokens:** 19.7 Trillion
 * **Context Length:** 128k
 * **Training Hardware:** NVIDIA B200 GPUs
-* **License:** **Solar-Apache License 2.0** (See [LICENSE](#license))
 * **Hardware Requirements:**
     * **Minimum:** 4x NVIDIA A100 (80GB)
@@ -52,7 +52,7 @@ This repository contains both model weights and code,
 which are licensed under different terms:
 1. MODEL WEIGHTS (*.safetensors)
-   Licensed under **Solar-Apache License 2.0**
    See: https://huggingface.co/upstage/Solar-Open-100B/blob/main/LICENSE
 2. CODE (*.py, *.json, *.jinja files)
@@ -215,7 +215,7 @@ vllm serve upstage/Solar-Open-100B \
 ## Public API Access
-The official API service for Solar Open is scheduled to launch publicly on **January**.
 * **Access:** Upstage Console (TBA)
 * **Documentation:** Upstage Console (TBA)

 - ko
 library_name: transformers
 license: other
+license_name: upstage-solar-license
 pipeline_tag: text-generation
 tags:
 - upstage
 # **Solar Open**
+**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Upstage Solar License** (see [LICENSE](#license) for details). 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.
 [**Technical Report**](https://huggingface.co/papers/2601.07022) | [**Project Page**](https://upstage.ai)
 * **Pre-training Tokens:** 19.7 Trillion
 * **Context Length:** 128k
 * **Training Hardware:** NVIDIA B200 GPUs
+* **License:** **Upstage Solar License** (See [LICENSE](#license))
 * **Hardware Requirements:**
     * **Minimum:** 4x NVIDIA A100 (80GB)
 which are licensed under different terms:
 1. MODEL WEIGHTS (*.safetensors)
+   Licensed under **Upstage Solar License**
    See: https://huggingface.co/upstage/Solar-Open-100B/blob/main/LICENSE
 2. CODE (*.py, *.json, *.jinja files)
 ## Public API Access
+The official API service for Solar Open is scheduled to launch publicly in **January**.
 * **Access:** Upstage Console (TBA)
 * **Documentation:** Upstage Console (TBA)