--- 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](assets/solar-open2.gif) # 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://arxiv.org/abs/2607.20062) | [**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/) ### Highlights ![Highlights_en](assets/Highlights_en.png) ![Highlights_kr](assets/Highlights_kr.png) - **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**
250B-A15B | **Solar Open 100B**
102B-A12B | **Command A+**
218B-A25B | **Mistral Medium 3.5**
128B dense, high | **MiMo-V2.5**
310B-A15B | **DeepSeek-V4-Flash**
284B-A13B, max | | --------------------- | ----------------------------: | -------------------------------: | --------------------------: | -----------------------------------------: | -------------------------: | --------------------------------------: | | **Know. & Reasoning** | | | | | | | | MMLU-Pro | **86.2** | 80.4 | 79.0 | 81.2 | 84.6 | 85.9 | | GPQA-Diamond | 86.3 | 66.2 | 75.6 | 77.5 | 83.0 | **88.9** | | HLE (w/o tools) | 28.8 | 11.5 | 11.4 | 12.8 | 24.3 | **32.3** | | LiveCodeBench (v6) | **92.4** | 56.5 | 86.1 | 84.9 | 89.1 | 92.3 | | ArtifactsBench | 55.9 | 43.4 | 42.8 | 49.8 | 59.3 | **61.0** | | HMMT2602 | 93.9 | 68.9 | 73.5 | 62.9 | 61.4 | **94.7** | | AIME2026 | 95.7 | 87.7 | 96.0 | 89.0 | 92.3 | **97.0** | | **IF / Long** | | | | | | | | Multi-Challenge | 61.0 | 40.5 | 45.8 | 49.8 | 39.0 | **62.0** | | IFBench | 80.0 | 57.7 | 73.9 | 69.0 | 67.1 | **80.3** | | AA-LCR | 62.3 | 36.0 | 46.0 | 61.0 | 62.7 | **63.7** | | **Agent** | | | | | | | | SWE-Bench Verified | 70.4 | 15.4 | 14.4 | 69.6 | 73.0 | **73.8** | | Terminal Bench Hard | 28.3 | 2.3 | 25.0 | 33.3 | **41.7** | 34.1 | | APEX-Agents | **16.6** | 2.4 | 1.6 | 6.1 | 13.4 | 13.2 | | MCP-Atlas | 58.2 | 34.4 | 27.2 | 30.7 | **63.9** | 58.2 | | τ³ (banking) | 19.6 | 7.4 | 5.8 | 5.8 | 8.7 | **22.3** | | GDPval-AA v2 (ELO) | 1128 | – | 712 | 929 | 1145 | **1187** | ### Korean Benchmarks | Benchmark | **Solar Open 2**
250B-A15B | **Solar Open 100B**
102B-A12B | **MiMo-V2.5**
310B-A15B | **DeepSeek-V4-Flash**
284B-A13B, max | **Claude Haiku 4.5**
closed | **GPT-5.4 mini**
closed | | ------------ | ----------------------------: | --------------------------------: | -------------------------: | --------------------------------------: | -----------------------------: | -------------------------: | | KMMLU-Pro | 78.4 | 64.0 | 69.1 | **78.9** | 67.9 | 78.1 | | CLIcK | **90.7** | 78.9 | 78.4 | 89.2 | 53.5 | 89.6 | | HAE-RAE v1.1 | **73.8** | 73.3 | 61.7 | 73.1 | 38.5 | 69.4 | | Ko-AIME’25† | 97.7 | 80.0 | 88.0 | **98.0** | 81.7 | 90.7 | | HRM8K | 92.2 | 87.6 | 90.7 | **93.4** | 90.6 | 91.3 | | KBank-MMLU† | **80.8** | 65.5 | 71.0 | 79.5 | 68.9 | 79.0 | | KBL | **75.5** | 65.5 | 69.8 | 72.8 | 69.9 | 75.3 | | KorMedMCQA | 93.0 | 84.4 | 87.7 | 94.1 | 87.0 | **94.2** | | Ko-GDPval† | **86.8** | 3.4 | 81.0 | 85.0 | 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{solaropen2-2026, title={Solar Open 2 Technical Report}, author={Upstage AI}, year={2026}, eprint={2607.20062}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2607.20062} } ``` ---