README.md

---
language:
- en
- ko
license: other
license_name: solar-apache-2.0
tags:
- upstage
- solar
- moe
- 100b
- llm
---

<p align="center">
  <img src="./Solar-Open-100B.png" alt="Solar Open Model" width="100%">
</p>

# **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.

## 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.

## 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:** **Solar-Apache License 2.0** (See [LICENSE](#license))
* **Hardware Requirements:**
    * **Minimum:** 4x NVIDIA A100 (80GB)

For more details, please refer to [Solar Open Technical Report](solar-open-technical-report.pdf).

## License
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)
   Licensed under **Apache License 2.0**
   See: https://www.apache.org/licenses/LICENSE-2.0

## Performance

### Korean Benchmarks

| Category | Benchmarks | Model Name (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 | Model Name (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:

```bash
pip install -U transformers kernels torch accelerate
```

Run inference with the following code:

```python
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",
    trust_remote_code=True,
)

# 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`.

```bash
# 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
```bash
uv venv --python 3.12 --seed
source .venv/bin/activate
```

Install Solar Open's optimized vLLM
```bash
VLLM_PRECOMPILED_WHEEL_LOCATION="https://github.com/vllm-project/vllm/releases/download/v0.12.0/vllm-0.12.0-cp38-abi3-manylinux_2_31_x86_64.whl" \
VLLM_USE_PRECOMPILED=1 \
uv pip install git+https://github.com/UpstageAI/vllm.git@v0.12.0-solar-open
```

Start the vLLM server (For 8 GPUs)
```bash
vllm serve 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
```

## 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)

## Citation

If you use Solar Open in your research, please cite:

```bibtex
@misc{solar-open-2025,
    title={Solar Open: Scaling Upstage's LLM Capabilities with MoE},
    author={Upstage AI},
    year={2025},
    url={https://huggingface.co/Upstage/Solar-Open-100B}
}
```