upstage/Solar-Open-100B

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** | **Project Page**

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.
• Massive Training Scale: Pre-trained on 19.7 trillion tokens, ensuring broad knowledge coverage and robust reasoning capabilities across various domains.
• Quantized Version Available: An official INT4 quantized model is provided by NotaAI and available at `nota-ai/Solar-Open-100B-NotaMoEQuant-Int4`.

Model Overview

• Model Name: Solar Open 100B
• Hugging Face ID: Upstage/Solar-Open-100B
• Architecture: Mixture-of-Experts (MoE)
• Total Parameters: 102.6B
• Active Parameters: 12B (per token)
• Experts: 129 Experts (top 8 among 128 Routed + 1 Shared)
• 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)

For more details, please refer to the Solar Open Technical Report.

License

This repository contains both model weights and code, 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) Licensed under Apache License 2.0 See: https://www.apache.org/licenses/LICENSE-2.0

Performance

Korean Benchmarks

| Category | Benchmarks | Solar Open (102B) | gpt-oss-120b (117B, high) | gpt-oss-120b (117B, medium) | GLM-4.5-Air (110B) | | :--- | :--- | :---: | :---: | :---: | :---: | | General | KMMLU | 73.0 | 72.7 | 70.3 | 70.2 | | | KMMLU-Pro | 64.0 | 62.6 | 60.5 | 60.7 | | | CLIcK | 78.9 | 77.2 | 72.9 | 48.3 | | | HAE-RAE v1.1 | 73.3 | 70.8 | 69.6 | 42.6 | | | KoBALT | 44.3 | 52.6 | 45.0 | 40.3 | | Finance | KBankMMLU (in-house) | 65.5 | 62.5 | 61.5 | 64.7 | | Law | KBL | 65.5 | 62.8 | 60.1 | 60.6 | | Medical | KorMedMCQA | 84.4 | 75.8 | 76.3 | 80.5 | | Math | Ko-AIME 2024 (in-house) | 80.3 | 90.0 | 76.7 | 80.0 | | | Ko-AIME 2025 (in-house) | 80.0 | 90.0 | 70.0 | 83.3 | | | HRM8K | 87.6 | 89.5 | 84.8 | 86.0 | | IF | Ko-IFEval | 87.5 | 93.2 | 86.7 | 79.5 | | Preference | Ko Arena Hard v2 (in-house) | 79.9 | 79.5 | 73.8 | 60.4 |

English Benchmarks

| Category | Benchmarks | Solar Open (102B) | gpt-oss-120b (117B, high) | gpt-oss-120b (117B, medium) | GLM-4.5-Air (110B) | | :--- | :--- | :---: | :---: | :---: | :---: | | General | MMLU | 88.2 | 88.6 | 87.9 | 83.3 | | | MMLU-Pro | 80.4 | 80.4 | 78.6 | 81.4 | | | GPQA-Diamond | 68.1 | 78.0 | 69.4 | 75.8 | | | HLE (text only) | 10.5 | 18.4 | 7.23 | 10.8 | | Math | AIME 2024 | 91.7 | 94.3 | 77.7 | 88.7 | | | AIME 2025 | 84.3 | 91.7 | 75.0 | 82.7 | | | HMMT 2025 (Feb) | 73.3 | 80.0 | 63.3 | 66.7 | | | HMMT 2025 (Nov) | 80.0 | 73.3 | 66.7 | 70.0 | | Code | LiveCodeBench (v1–v6 cumul) | 74.2 | 89.9 | 82.8 | 71.9 | | IF | IFBench | 53.7 | 70.8 | 61.2 | 37.8 | | | IFEval | 88.0 | 91.4 | 86.5 | 86.5 | | Preference | Arena Hard v2 | 74.8 | 79.6 | 72.7 | 62.5 | | | Writing Bench | 7.51 | 6.61 | 6.55 | 7.40 | | Agent | Tau² Airline | 52.4 | 56.0 | 52.8 | 60.8 | | | Tau² Telecom | 55.6 | 57.7 | 47.4 | 28.1 | | | Tau² Retail | 59.3 | 76.5 | 68.4 | 71.9 | | Long | AA-LCR | 35.0 | 48.3 | 45.0 | 37.3 |

Inference Quickstart

We recommend using the following generation parameters:

temperature=0.8
top_p=0.95
top_k=50

Transformers

Install the required dependencies:

pip install -U "transformers>=5.0" kernels torch accelerate

Run inference with the following code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "upstage/Solar-Open-100B"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=MODEL_ID,
torch_dtype=torch.bfloat16,
device_map="auto",
)

# Prepare input
messages = [{"role": "user", "content": "who are you?"}]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
inputs = inputs.to(model.device)

# Generate response
generated_ids = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.8,
top_p=0.95,
top_k=50,
do_sample=True,
)
generated_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1] :])
print(generated_text)

vLLM

Option 1: Using Docker (Highly Recommended)

Docker is the recommended deployment method for running Solar-Open-100B.

# For 8 GPUs
docker run --gpus all \
--ipc=host \
-p 8000:8000 \
upstage/vllm-solar-open:latest \
upstage/Solar-Open-100B \
--trust-remote-code \
--enable-auto-tool-choice \
--tool-call-parser solar_open \
--reasoning-parser solar_open \
--logits-processors vllm.model_executor.models.parallel_tool_call_logits_processor:ParallelToolCallLogitsProcessor \
--logits-processors vllm.model_executor.models.solar_open_logits_processor:SolarOpenTemplateLogitsProcessor \
--tensor-parallel-size 8

Option 2: Installing from Source

For development, debugging, custom modifications or offline inference, Solar Open can also be run using a source installation of vLLM. We recommend using [uv](https://docs.astral.sh/uv/) for environment management and dependency resolution.

Create and activate a Python virtual environment

uv venv --python 3.12 --seed
source .venv/bin/activate

Install Solar Open's optimized vLLM