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language:
- en
- ko
- ja
library_name: transformers
license: other
license_name: upstage-solar-license
license_link: LICENSE
pipeline_tag: text-generation
tags:
- upstage
- solar
- moe
- llm
- vllm
---
# Solar Open 2
Solar Open 2 is Upstage’s 250B-A15B open-weight large language model, built for agentic use cases such as office productivity, document-intensive work, and coding. Its Hybrid-Attention Mixture-of-Experts (MoE) architecture with linear attention delivers highly efficient inference even in long-context settings.
[**Technical Report**](https://huggingface.co/upstage/Solar-Open2-250B/blob/main/Solar_Open_2_Tech_Report.pdf) | [**Blog**](https://www.upstage.ai/blog/en/solar-open-2?utm_source=hf&utm_medium=referral&utm_campaign=so2-launch&utm_content=modelcard) | [**Upstage Website**](https://www.upstage.ai/) | [**Try Demo (~7/31)**](https://open2-beta.upstage.ai/)
### Highlights
- **Agentic Specialist:** Purpose-built for agentic workflows — tool calling, multi-step reasoning, and end-to-end task execution. Competitive with the strongest open-weight models on agent benchmarks.
- **Minimal Inference Cost:** A 250B-parameter MoE that activates only 15B per token, built on a hybrid attention stack that interleaves three linear-attention layers with one softmax-attention layer — large-model capacity at small-model inference cost.
- **1M-Token Context:** The linear-attention layers encode token order intrinsically in their recurrent state, so positional encoding is removed entirely (NoPE), lifting the RoPE extrapolation limit. Only 12 of the 48 layers keep a KV cache, holding long-context memory to roughly a quarter of an all-softmax model of the same shape.
- **Efficiently Trained at Low Cost:** Initialized by selective weight transfer from Solar Open 1 (102B) — only the 2.3% of weights that survive the architectural change are carried over, and everything else is randomly initialized — which raises the starting point and accelerates early convergence at 250B scale.
- **Multilingual:** English, Korean, and Japanese.
---
## Model Overview
| Field | Value |
| ------------------------------- | ----------------------------------------------------------------------- |
| Model Name | Solar Open 2 (250B-A15B) |
| Architecture | Hybrid-Attention Mixture-of-Experts (MoE) |
| Total Parameters | 250B (250,287,794,944) |
| Active Parameters | 15B (per token) |
| Layers | 48 |
| Hidden Size | 4096 |
| Attention | Hybrid — Softmax + Linear Attention, pattern `[Softmax, Linear×3] × 12` |
| Position Encoding | NoPE (no rotary positional encoding) |
| Number of Attention Heads (GQA) | (Softmax) 64 query / 8 KV, (Linear) 64 query |
| Number of Experts | 321 (320 routed + 1 shared) |
| Number of Activated Experts | 8 routed (top-8) + 1 shared |
| Vocabulary | 196,608 |
| Context Length | 1M |
| Pre-training Tokens | ~12 Trillion |
| Supported Languages | English, Korean, Japanese |
| Training Hardware | NVIDIA B200 GPUs |
| Training GPU Time | 2M GPU Hours |
| License | Upstage Solar License (see [LICENSE](https://huggingface.co/upstage/Solar-Open2-250B/blob/main/LICENSE)) |
| Hardware Requirements | Minimum: H200 \* 4ea / Recommended: H200 \* 8ea |
---
## Performance
### English Benchmarks
| Benchmark | **Solar Open 2**<br>250B-A15B | **Solar Open 100B**<br>102B-A12B | **Command A+**<br>218B-A25B | **Mistral Medium 3.5**<br>128B dense, high | **MiMo-V2.5**<br>310B-A15B | **DeepSeek-V4-Flash**<br>284B-A13B, max |
| --------------------- | ----------------------------: | -------------------------------: | --------------------------: | -----------------------------------------: | -------------------------: | --------------------------------------: |
| **Know. & Reasoning** | | | | | | |
| MMLU-Pro | **86.2** | 80.4 | 79.0 | 81.2 | 84.6 | <u>85.9</u> |
| GPQA-Diamond | <u>86.3</u> | 66.2 | 75.6 | 77.5 | 83.0 | **88.9** |
| HLE (w/o tools) | <u>28.8</u> | 11.5 | 11.4 | 12.8 | 24.3 | **32.3** |
| LiveCodeBench (v6) | **92.4** | 56.5 | 86.1 | 84.9 | 89.1 | <u>92.3</u> |
| ArtifactsBench | 55.9 | 43.4 | 42.8 | 49.8 | <u>59.3</u> | **61.0** |
| HMMT2602 | <u>93.9</u> | 68.9 | 73.5 | 62.9 | 61.4 | **94.7** |
| AIME2026 | 95.7 | 87.7 | <u>96.0</u> | 89.0 | 92.3 | **97.0** |
| **IF / Long** | | | | | | |
| Multi-Challenge | <u>61.0</u> | 40.5 | 45.8 | 49.8 | 39.0 | **62.0** |
| IFBench | <u>80.0</u> | 57.7 | 73.9 | 69.0 | 67.1 | **80.3** |
| AA-LCR | 62.3 | 36.0 | 46.0 | 61.0 | <u>62.7</u> | **63.7** |
| **Agent** | | | | | | |
| SWE-Bench Verified | 70.4 | 15.4 | 14.4 | 69.6 | <u>73.0</u> | **73.8** |
| Terminal Bench Hard | 28.3 | 2.3 | 25.0 | 33.3 | **41.7** | <u>34.1</u> |
| APEX-Agents | **16.6** | 2.4 | 1.6 | 6.1 | <u>13.4</u> | 13.2 |
| MCP-Atlas | <u>58.2</u> | 34.4 | 27.2 | 30.7 | **63.9** | <u>58.2</u> |
| τ³ (banking) | <u>19.6</u> | 7.4 | 5.8 | 5.8 | 8.7 | **22.3** |
| GDPval-AA v2 (ELO) | 1128 | – | 712 | 929 | <u>1145</u> | **1187** |
### Korean Benchmarks
| Benchmark | **Solar Open 2**<br>250B-A15B | **Solar Open 100B**<br>102B-A12B | **MiMo-V2.5**<br>310B-A15B | **DeepSeek-V4-Flash**<br>284B-A13B, max | **Claude Haiku 4.5**<br>closed | **GPT-5.4 mini**<br>closed |
| ------------ | ----------------------------: | --------------------------------: | -------------------------: | --------------------------------------: | -----------------------------: | -------------------------: |
| KMMLU-Pro | <u>78.4</u> | 64.0 | 69.1 | **78.9** | 67.9 | 78.1 |
| CLIcK | **90.7** | 78.9 | 78.4 | 89.2 | 53.5 | <u>89.6</u> |
| HAE-RAE v1.1 | **73.8** | <u>73.3</u> | 61.7 | 73.1 | 38.5 | 69.4 |
| Ko-AIME’25† | <u>97.7</u> | 80.0 | 88.0 | **98.0** | 81.7 | 90.7 |
| HRM8K | <u>92.2</u> | 87.6 | 90.7 | **93.4** | 90.6 | 91.3 |
| KBank-MMLU† | **80.8** | 65.5 | 71.0 | <u>79.5</u> | 68.9 | 79.0 |
| KBL | **75.5** | 65.5 | 69.8 | 72.8 | 69.9 | <u>75.3</u> |
| KorMedMCQA | 93.0 | 84.4 | 87.7 | <u>94.1</u> | 87.0 | **94.2** |
| Ko-GDPval† | **86.8** | 3.4 | 81.0 | <u>85.0</u> | 68.3 | 59.4 |
† in-house benchmarks.
---
## Quickstart
The examples below assume 8 GPUs with at least 141 GB of memory each, such as NVIDIA H200 or B200 GPUs.
Actual memory requirements depend on the context length and serving settings.
### Transformers
Use the [Upstage Transformers branch with native Solar Open 2 support](https://github.com/upstageAI/transformers/tree/v5.14.1-solar-open2) for local experimentation. For production serving, we recommend vLLM.
Install the dependencies:
> Install a CUDA-enabled PyTorch build for your platform before running this command. `fla-core` enables the optimized KDA kernels; without it, Transformers uses a substantially slower PyTorch fallback.
```bash
python -m pip install -U \
"git+https://github.com/upstageAI/transformers.git@v5.14.1-solar-open2" \
"fla-core[cuda]>=0.5.1" \
accelerate einops
```
Run the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "upstage/Solar-Open2-250B"
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
dtype=torch.bfloat16,
trust_remote_code=False,
)
model.eval()
messages = [
{"role": "user", "content": "What is Upstage?"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
reasoning_effort="high",
think_render_option="preserved",
)
input_device = model.get_input_embeddings().weight.device
model_inputs = tokenizer(prompt, return_tensors="pt").to(input_device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768,
do_sample=True,
temperature=1.0,
top_p=1.0,
)
new_token_ids = generated_ids[0, model_inputs.input_ids.shape[-1] :].tolist()
think_end_id = tokenizer.convert_tokens_to_ids("<|think:end|>")
if think_end_id in new_token_ids:
# Split immediately after the final <|think:end|> token.
answer_start = len(new_token_ids) - new_token_ids[::-1].index(think_end_id)
else:
# No end marker usually means generation stopped while the model was reasoning.
answer_start = len(new_token_ids)
reasoning = tokenizer.decode(
new_token_ids[:answer_start],
skip_special_tokens=True,
).strip()
answer = tokenizer.decode(
new_token_ids[answer_start:],
skip_special_tokens=True,
).strip()
print("[reasoning]", reasoning)
print("[answer]", answer)
```
If the answer is empty, generation likely reached `max_new_tokens` before the reasoning block ended. Increase `max_new_tokens` and try again.
### Serving with vLLM (Recommended)
#### **Option 1: Docker**
The image below is based on vLLM v0.22.0 and CUDA 12.9.
```bash
docker run --rm --gpus all --ipc=host \
-p 8000:8000 \
-v "${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface" \
upstage/vllm-solar-open2 \
upstage/Solar-Open2-250B \
--served-model-name solar-open2-250b \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--moe-backend triton \
--default-chat-template-kwargs '{"think_render_option":"preserved"}' \
--reasoning-parser solar_open2 \
--tool-call-parser solar_open2 \
--enable-auto-tool-choice \
--logits-processors vllm.v1.sample.logits_processor.solar_open2:SolarOpen2TemplateLogitsProcessor
```
#### **Option 2: Install from source**
Install the [Upstage fork](https://github.com/UpstageAI/vllm/tree/v0.22.0-solar-open2) while reusing the matching vLLM v0.22.0 CUDA 12.9 wheel:
```bash
pip install -U uv
VLLM_PRECOMPILED_WHEEL_LOCATION="https://github.com/vllm-project/vllm/releases/download/v0.22.0/vllm-0.22.0%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl" \
VLLM_USE_PRECOMPILED=1 \
uv pip install --reinstall-package vllm --torch-backend=cu129 \
"git+https://github.com/UpstageAI/vllm.git@v0.22.0-solar-open2"
```
Start the server:
```bash
vllm serve upstage/Solar-Open2-250B \
--served-model-name solar-open2-250b \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--moe-backend triton \
--default-chat-template-kwargs '{"think_render_option":"preserved"}' \
--reasoning-parser solar_open2 \
--tool-call-parser solar_open2 \
--enable-auto-tool-choice \
--logits-processors vllm.v1.sample.logits_processor.solar_open2:SolarOpen2TemplateLogitsProcessor
```
Send a chat completion request:
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "solar-open2-250b",
"messages": [
{"role": "user", "content": "What is Upstage?"}
],
"max_tokens": 131584,
"temperature": 1.0,
"top_p": 1.0,
"reasoning_effort": "high"
}'
```
### Quantized Versions
Official quantized models by [NotaAI](https://huggingface.co/nota-ai) are available for deployment on smaller GPU configurations:
- [Solar-Open2-250B-Nota-INT4-GlobalPruned](https://huggingface.co/nota-ai/Solar-Open2-250B-Nota-INT4-GlobalPruned)
- [Solar-Open2-250B-Nota-NVFP4](https://huggingface.co/nota-ai/Solar-Open2-250B-Nota-NVFP4)
- [Solar-Open2-250B-Nota-INT4](https://huggingface.co/nota-ai/Solar-Open2-250B-Nota-INT4)
---
## Capabilities
### **Reasoning**
Use `reasoning_effort="high"` for reasoning and `reasoning_effort="none"` for a direct response. The recommended vLLM configuration limits a reasoning block to 131,072 tokens.
| Effort | Behavior |
| ------ | ----------------------------------- |
| `none` | Direct response |
| `high` | Reasoning, capped at 131,072 tokens |
`max_tokens` limits the complete response, including reasoning and the final answer, so leave room beyond the reasoning cap.
```python
from openai import OpenAI
client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
model="solar-open2-250b",
messages=[
{
"role": "user",
"content": "Prove that the square root of 2 is irrational.",
},
],
reasoning_effort="high",
temperature=1.0,
top_p=1.0,
max_tokens=131584,
)
# The reasoning trace is returned separately from the final answer.
print(response.choices[0].message.reasoning)
print(response.choices[0].message.content)
```
### **Tool Calling**
Tool calls follow the standard OpenAI function-calling interface. Start the server with `--tool-call-parser solar_open2` and `--enable-auto-tool-choice`.
```python
from openai import OpenAI
client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
},
"required": ["location"],
},
},
},
]
response = client.chat.completions.create(
model="solar-open2-250b",
messages=[
{
"role": "user",
"content": "What's the weather in Seoul?",
},
],
tools=tools,
)
print(response.choices[0].message.tool_calls)
```
## **Agentic Use**
Both Anthropic's Claude Code and Nous Research's Hermes Agent can run on Solar Open 2 served locally with vLLM (see the vLLM deployment guide). A single vLLM server exposes both interfaces: Claude Code connects through the Anthropic-compatible /v1/messages endpoint and Hermes Agent through the OpenAI-compatible /v1 endpoint (model id solar-open2-250b), each needing only a few environment variables or one provider entry — no setup script required. Tools exposed over the Model Context Protocol (MCP) reach the model through the same tool-calling interface, and both agents support MCP natively.
### **Claude Code**
vLLM exposes an Anthropic-compatible /v1/messages endpoint, so Claude Code connects directly — no proxy needed:
```
export ANTHROPIC_BASE_URL=http://localhost:8000
export ANTHROPIC_AUTH_TOKEN=dummy # any non-empty value
export ANTHROPIC_MODEL=solar-open2-250b
export ANTHROPIC_SMALL_FAST_MODEL=solar-open2-250b
claude
```
The model name must match the server's --served-model-name (solar-open2-250b).
Prerequisites: the Claude Code CLI installed and a running vLLM server.
### **Hermes Agent**
Register the local vLLM server as a custom OpenAI-compatible provider in ~/.hermes/config.yaml:
```yaml
model:
provider: custom
default: solar-open2-250b
base_url: http://localhost:8000/v1
api_key: dummy
```
---
## Best Practices
Recommended **client-side generation settings** (the values a client / API caller should send)
Solar Open 2 is a reasoning-capable model. Use `reasoning_effort="high"` for complex or agentic tasks. The recommended vLLM configuration preserves the reasoning trace.
| Parameter | Recommended | Notes |
| ------------------ | ----------- | --------------------------------------------------- |
| `reasoning_effort` | `high` | Recommended for complex reasoning and agentic tasks |
| `temperature` | 1.0 | |
| `top_p` | 1.0 | |
| `max_tokens` | up to 256K | Covers reasoning + output budget |
**Recommended settings by reasoning mode**
| Mode | temperature | top_p | max_tokens |
| ------------------------- | ----------- | ----- | ---------- |
| `reasoning_effort="none"` | 1.0 | 1.0 | up to 128K |
| `reasoning_effort="high"` | 1.0 | 1.0 | up to 256K |
- Set `max_tokens` high enough (up to 256K) — reasoning traces can be long and may otherwise truncate the answer.
- The reasoning trace is preserved by default (`think_render_option=preserved`).
- Multi-turn: Keep prior reasoning traces in the conversation history. The default `think_render_option=preserved` handles this automatically — do not strip reasoning from previous turns when constructing follow-up requests.
- **Parsing:** the OpenAI-compatible server returns reasoning in a separate `message.reasoning` field with local `transformers`, split the raw output on the reasoning markers yourself.
---
## License
Solar Open 2 is distributed under the [**Upstage Solar License**](https://huggingface.co/upstage/Solar-Open2-250B/blob/main/LICENSE).
**Key requirements for Derivative AI Models** (create / train / fine-tune / distill / improve using Solar Open 2):
- **Naming:** prefix your model name with "Solar" (e.g., `Solar-MyModel-v1`).
- **Attribution:** prominently display "Built with Solar" in related public-facing materials.
- **Notice:** include a copy of the Upstage Solar License with your derivative model.
---
## Citation
```bibtex
@misc{solar-open-2-2026,
title={Solar Open 2 Technical Report},
author={Upstage AI},
year={2026},
url={https://huggingface.co/upstage/Solar-Open2-250B}
}
```
---
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