| --- |
| 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} |
| } |
| ``` |
|
|
| --- |
|
|
