Release Solar Open2 250B

.gitattributes

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+assets/solar-open2.gif filter=lfs diff=lfs merge=lfs -text
+assets/highlights.webp filter=lfs diff=lfs merge=lfs -text
+Solar_Open_2_Tech_Report.pdf filter=lfs diff=lfs merge=lfs -text
+assets/Highlights_en.png filter=lfs diff=lfs merge=lfs -text
+assets/Highlights_kr.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,96 @@
+# Upstage Solar License
+
+# Preamble
+
+The 'Upstage Solar License' (hereinafter referred to as "this License") was established by Upstage Co., Ltd., incorporated under the laws of the Republic of Korea, to encourage the development of open-source software using Solar AI models, and is not affiliated with the Apache Software Foundation.
+
+This License basically adopts all provisions of the Apache License, Version 2.0 (hereinafter referred to as "Apache License 2.0"), including the principle of allowing commercial use, but prescribes minimum strategic conditions for the global expansion of AI technology and the sustainable development of the AI ecosystem.
+
+The key additional condition, as specified in Section 4(e), is that if you distribute a "Derivative AI Model" based on the "Work", you must specify the 'Solar' brand. This applies as an exception to Section 6 (Trademarks) of the Apache License 2.0.
+
+# TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
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+5\. Submission of Contributions.  
+Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
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+9\. Accepting Warranty or Additional Liability.  
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+
+END OF TERMS AND CONDITIONS
+
+# APPENDIX: How to apply the Upstage Solar License to your work.
+
+To apply the Upstage Solar License to your work, attach the following boilerplate notice, with the fields enclosed by brackets replaced with your own identifying information. (Don't include the brackets\!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives.
+
+Copyright \[yyyy\] \[Upstage AI (or other copyright owner)\]
+
+Licensed under the Upstage Solar License (the "License");  
+you may not use this file except in compliance with the License.  
+You may obtain a copy of the License at
+
+   https://huggingface.co/Upstage/Solar-Open-100B/blob/main/LICENSE
+
+Unless required by applicable law or agreed to in writing, software  
+distributed under the License is distributed on an "AS IS" BASIS,  
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
+See the License for the specific language governing permissions and  
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+
+This work is based on or derived from materials licensed under the  
+Upstage Solar License.  
+If you distribute or make available a Derivative AI Model (as defined  
+in Section 4(e) of the License), your model name must begin with "Solar"  
+and you must prominently display "Built with Solar" in associated  
+documentation or interfaces.  

README.md

@@ -0,0 +1,443 @@
+---
+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://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
+
+![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**<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}
+}
+```
+
+---

Solar_Open_2_Tech_Report.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adb9173134a2564af9c294752ae6eb264ee59be3160a2ed5c0d45852c5329e62
+size 9827344

chat_template.jinja

@@ -0,0 +1,234 @@
+{#- ======== Template Parameters ========  #}
+{%- set add_generation_prompt = add_generation_prompt if add_generation_prompt is defined else true %}
+{%- set provider_system_prompt = provider_system_prompt if provider_system_prompt else false %}
+{%- set reasoning_effort = reasoning_effort if reasoning_effort is defined else "high" %}
+{%- set think_render_option = think_render_option if think_render_option is defined else "interleaved" %}
+
+{#- ======== System Block State ========  #}
+{%- set sys_ns = namespace(is_first_block=true) -%}
+
+{#- ======== Find last user message index ========  #}
+{%- set last_user_idx = namespace(value=-1) -%}
+{%- for message in messages -%}
+    {%- if message.role == 'user' -%}
+        {%- set last_user_idx.value = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- ======== System messages renderers ========  #}
+{%- macro render_system_message(user_system_messages) %}
+    {%- if provider_system_prompt %}
+        {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+        {%- set sys_ns.is_first_block = false %}
+        {{- "## Provider System Prompt\n\nYou are Solar Open2 250B, a large language model trained by Upstage AI, a Korean startup. Your knowledge cutoff is 2026-02. The current date is " + strftime_now("%Y-%m-%d") + "." }}
+    {%- endif -%}
+    {%- if user_system_messages %}
+        {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+        {%- set sys_ns.is_first_block = false %}
+        {{- "## System Prompt" }}
+        {%- for system_message in user_system_messages %}
+            {{- "\n\n" }}
+            {{- system_message }}
+        {%- endfor %}
+    {%- endif -%}
+{%- endmacro %}
+
+{%- macro render_tool_instruction(tools, is_last_block) %}
+    {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+    {%- set sys_ns.is_first_block = false %}
+    {{- "## Tools\n- You may invoke one or more tools to assist with the user's query." }}
+    {{- "\n\n### Available Tools\n" }}
+    {%- for tool in tools %}
+        {{- "<|tool:start|>" }}
+        {{- tool.function | tojson }}
+        {{- "<|tool:end|>\n" }}
+    {%- endfor %}
+    {{- "\n### Tool Call Instruction\n" }}
+    {{- "- If using a tool, any reasoning must strictly precede the call. Do not append any text after the tool call.\n" }}
+    {{- "- If no tool is required, answer directly from your knowledge without ever mentioning the availability or absence of tools.\n" }}
+    {{- "- Each tool call MUST use this following format: <|tool_call:start|>{example-tool-name}\n<|tool_arg:start|>{example-key-name-1}<|tool_arg:value|>{example-value-1}<|tool_arg:end|>\n<|tool_arg:start|>{example-key-name-2}<|tool_arg:value|>{example-value-2}<|tool_arg:end|>\n<|tool_call:end|>\n" }}
+{%- endmacro %}
+
+{#-
+Input convention for `response_format`:
+- Per the OpenAI Chat Completions API, `response_format` is an object of one of:
+    {"type": "json_schema", "json_schema": {"name": ..., "schema": {...}, "strict": ...}}
+    {"type": "json_object"}
+- Only `response_format.json_schema.schema` (the inner JSON Schema) is injected
+  into the prompt; the OpenAI wrapper fields (`name`, `strict`, ...) are dropped.
+- For `json_object`, no schema exists, so we emit a generic JSON-only instruction.
+- vLLM does NOT pass `response_format` to chat templates by default; it routes it
+  to guided decoding instead. This template/whale opts into prompt injection,
+  so callers serving the model directly via vLLM must opt in via
+  `chat_template_kwargs` (or rely on whale's normalization).
+-#}
+{%- macro render_json_response_format_instruction(response_format) %}
+    {%- if not sys_ns.is_first_block %}{{- "\n\n" }}{%- endif %}
+    {%- set sys_ns.is_first_block = false %}
+    {{- "## Output Format Constraint" }}
+    {%- if response_format.type == "json_schema" %}
+        {{- "\n\n- Your final response should follow the JSON schema:\n```json\n" }}
+        {{- response_format.json_schema.schema | tojson }}
+        {{- "\n```\n- Please ensure your answers adhere to this format and do not contain any unnecessary text." }}
+    {%- elif response_format.type == "json_object" %}
+        {{- "\n\n- Your final response must be a valid JSON object." }}
+        {{- "\n- Do not include any text outside the JSON object." }}
+    {%- endif %}
+{%- endmacro %}
+
+{#-
+Input convention for `tool_arguments`:
+- Must be a mapping (dict), NOT a JSON-encoded string.
+- The OpenAI API spec defines `tool_calls[].function.arguments` as a JSON string,
+  but vLLM, HuggingFace transformers tool-use templates (Qwen, Hermes, Llama, ...),
+  and this template all expect the value to be parsed into a dict before rendering.
+- vLLM normalizes this in `_postprocess_messages()` (vllm/entrypoints/chat_utils.py).
+- Callers (whale, training pipelines that call `tokenizer.apply_chat_template`
+  directly, ...) are responsible for parsing the JSON string into a dict before
+  invoking the template.
+-#}
+{%- macro render_tool_arguments(tool_arguments) %}
+    {%- for args_name, args_value in tool_arguments|items %}
+        {{- "<|tool_arg:start|>"+ args_name + "<|tool_arg:value|>"}}
+        {%- set args_value = args_value if args_value is string else (args_value | tojson | safe) %}
+        {{- args_value }}
+        {{- "<|tool_arg:end|>\n" }}
+    {%- endfor %}
+{%- endmacro %}
+
+{#- ======== Render system message ========  #}
+{%- set ns = namespace(system_messages=[]) -%}
+{%- for message in messages -%}
+    {%- if message.role == 'system' -%}
+        {%- set ns.system_messages = ns.system_messages + [message.content] -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if ns.system_messages or provider_system_prompt or tools or response_format -%}
+    {{- "<|im:start|>system<|im:content|>" }}
+        {{- render_system_message(ns.system_messages) }}
+        {%- if tools -%}
+            {{- render_tool_instruction(tools, not response_format) }}
+        {%- endif %}
+        {%- if response_format -%}
+            {{- render_json_response_format_instruction(response_format) }}
+        {%- endif %}
+    {{- "<|im:end|>\n" }}
+{%- endif -%}
+
+{#- ======== Render main messages ========  #}
+{%- for message in messages -%}
+    {%- if message.role == 'user' -%}
+         {{- "<|im:start|>user<|im:content|>" + message.content + "<|im:end|>\n" }}
+    {%- elif message.role == 'tool' -%}
+        {%- if not message.tool_call_id -%}
+            {{- raise_exception("tool message is missing required 'tool_call_id' field") -}}
+        {%- endif -%}
+        {%- set prev_is_tool = loop.index0 > 0 and messages[loop.index0 - 1].role == 'tool' -%}
+        {#- Only render at the start of a contiguous tool block; subsequent tool messages -#}
+        {#- are already emitted (reordered) within the first one. -#}
+        {%- if not prev_is_tool -%}
+            {%- set start_idx = loop.index0 -%}
+            {#- Collect contiguous tool messages starting at start_idx -#}
+            {%- set tool_ns = namespace(items=[], scanning=true) -%}
+            {%- for j in range(start_idx, messages | length) -%}
+                {%- if tool_ns.scanning -%}
+                    {%- if messages[j].role == 'tool' -%}
+                        {%- set tool_ns.items = tool_ns.items + [messages[j]] -%}
+                    {%- else -%}
+                        {%- set tool_ns.scanning = false -%}
+                    {%- endif -%}
+                {%- endif -%}
+            {%- endfor -%}
+
+            {{- "<|im:start|>tool<|im:content|>" }}
+
+            {%- set prev_msg = messages[start_idx - 1] if start_idx > 0 else none -%}
+            {%- if prev_msg and prev_msg.role == 'assistant' and prev_msg.tool_calls -%}
+                {#- Render tool responses in the order of the preceding assistant's tool_calls ids. -#}
+                {%- set sep_ns = namespace(first=true, rendered_ids=[]) -%}
+                {%- for tool_call in prev_msg.tool_calls -%}
+                    {%- for tool_msg in tool_ns.items -%}
+                        {%- if tool_msg.tool_call_id == tool_call.id -%}
+                            {%- if not sep_ns.first -%}{{- "\n" }}{%- endif -%}
+                            {%- set sep_ns.first = false -%}
+                            {%- set sep_ns.rendered_ids = sep_ns.rendered_ids + [tool_msg.tool_call_id] -%}
+                            {{- "<|tool_response:start|>" + tool_msg.content + "<|tool_response:end|>" }}
+                        {%- endif -%}
+                    {%- endfor -%}
+                {%- endfor -%}
+                {#- Append any orphan tool messages (no matching id) in their original order. -#}
+                {%- for tool_msg in tool_ns.items -%}
+                    {%- if tool_msg.tool_call_id not in sep_ns.rendered_ids -%}
+                        {%- if not sep_ns.first -%}{{- "\n" }}{%- endif -%}
+                        {%- set sep_ns.first = false -%}
+                        {{- "<|tool_response:start|>" + tool_msg.content + "<|tool_response:end|>" }}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- else -%}
+                {%- for tool_msg in tool_ns.items -%}
+                    {%- if not loop.first -%}{{- "\n" }}{%- endif -%}
+                    {{- "<|tool_response:start|>" + tool_msg.content + "<|tool_response:end|>" }}
+                {%- endfor -%}
+            {%- endif -%}
+
+            {{- "\n<|im:end|>\n" }}
+        {%- endif -%}
+    {%- elif message.role == 'assistant' -%}
+        {{- "<|im:start|>assistant<|im:content|>" }}
+
+        {#- ======== Resolve reasoning field (reasoning or reasoning_content) ========  #}
+        {%- if message.reasoning is defined -%}
+            {%- set reasoning = message.reasoning -%}
+        {%- elif message.reasoning_content is defined -%}
+            {%- set reasoning = message.reasoning_content -%}
+        {%- else -%}
+            {%- set reasoning = none -%}
+        {%- endif -%}
+
+        {#- ======== Assistant Thinking ========  #}
+        {%- if think_render_option == "preserved" -%}
+            {%- if reasoning -%}
+                {{- "<|think:start|>" + reasoning + "<|think:end|>" }}
+            {%- else -%}
+                {{- "<|think:start|><|think:end|>" }}
+            {%- endif -%}
+        {%- elif think_render_option == "interleaved" -%}
+            {%- if reasoning and loop.index0 > last_user_idx.value -%}
+                {{- "<|think:start|>" + reasoning + "<|think:end|>" }}
+            {%- else -%}
+                {{- "<|think:start|><|think:end|>" }}
+            {%- endif -%}
+        {%- endif -%}
+
+        {#- ======== Assistant Messages ========  #}
+        {%- if message.content -%}
+            {{- message.content }}
+        {%- endif -%}
+
+        {#- ======== Assistant Tool calls ========  #}
+        {%- if message.tool_calls -%}
+            {%- for tool_call in message.tool_calls -%}
+                {%- if not tool_call.id -%}
+                    {{- raise_exception("assistant tool_call is missing required 'id' field") -}}
+                {%- endif -%}
+                {%- if not loop.first -%}{{- "\n" }}{%- endif -%}
+                {{- "<|tool_call:start|>" + tool_call.function.name + "\n" }}
+                {%- if tool_call.function.arguments is defined %}
+                    {{- render_tool_arguments(tool_call.function.arguments) }}
+                {%- endif %}
+                {{- "<|tool_call:end|>" }}
+            {%- endfor -%}
+            {{- "\n" }}
+        {%- endif -%}
+        {{- "<|im:end|>\n" }}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if reasoning_effort in ["medium", "high", "xhigh"] -%}
+        {{- "<|im:start|>assistant<|im:content|><|think:start|>" }}
+    {%- else -%}
+        {{- "<|im:start|>assistant<|im:content|><|think:start|><|think:end|>" }}
+    {%- endif -%}
+{%- endif -%}

config.json

@@ -0,0 +1,55 @@
+{
+    "model_type": "solar_open2",
+    "architectures": [
+        "SolarOpen2ForCausalLM"
+    ],
+    "pad_token_id": 2,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "partial_rotary_factor": 1.0,
+    "linear_attn_config": {
+        "short_conv_kernel_size": 4,
+        "head_dim": 128,
+        "num_heads": 64,
+        "num_kv_heads": null
+    },
+    "hidden_size": 4096,
+    "num_hidden_layers": 48,
+    "num_attention_heads": 64,
+    "head_dim": 128,
+    "num_key_value_heads": 8,
+    "vocab_size": 196608,
+    "intermediate_size": 10240,
+    "moe_intermediate_size": 1280,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 10000,
+    "tie_word_embeddings": false,
+    "max_position_embeddings": 1048576,
+    "first_k_dense_replace": 0,
+    "use_rope": false,
+    "gqa_interval": 3,
+    "gqa_layers": [
+        0,
+        4,
+        8,
+        12,
+        16,
+        20,
+        24,
+        28,
+        32,
+        36,
+        40,
+        44
+    ],
+    "use_gqa_gate": true,
+    "kda_use_full_proj": false,
+    "kda_allow_neg_eigval": true,
+    "n_routed_experts": 320,
+    "n_shared_experts": 1,
+    "norm_topk_prob": true,
+    "routed_scaling_factor": 1.0,
+    "num_experts_per_tok": 8,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "5.5.4"
+}

generation_config.json

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+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": [
+    2,
+    129
+  ],
+  "transformers_version": "4.57.6"
+}

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