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+<!--Copyright 2026 The Upstage Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+*This model was published in HF papers on 2026-01-11 and contributed to Hugging Face Transformers on 2026-01-22.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# SolarOpen
+
+## Overview
+
+The SolarOpen model was proposed in [Solar Open Technical Report](https://huggingface.co/papers/2601.07022) by Upstage Team.
+
+The abstract from the paper is the following:
+
+We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages.
+Solar Open demonstrates a systematic methodology for building competitive LLMs by addressing three interconnected challenges.
+First, to train effectively despite data scarcity for underserved languages, we synthesize 4.5T tokens of high-quality, domain-specific, and RL-oriented
+data.
+Second, we coordinate this data through a progressive curriculum jointly optimizing composition, quality thresholds, and domain coverage across 20 trillion tokens.
+Third, to enable reasoning capabilities through scalable RL, we apply our proposed framework SnapPO for efficient optimization.
+Across benchmarks in English and Korean, Solar Open achieves competitive performance, demonstrating the effectiveness of this methodology for underserved language AI development.
+
+## Usage Tips
+
+Recommended inference parameters for optimal performance:
+
+```
+temperature=0.8
+top_p=0.95
+top_k=50
+```
+
+**Examples**
+
+```python
+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,
+    device_map="auto",
+)
+
+# 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)
+```
+
+This model was contributed by [SSON9](https://huggingface.co/SSON9) from [Upstage](https://huggingface.co/upstage).
+
+## SolarOpenConfig
+
+[[autodoc]] SolarOpenConfig
+
+## SolarOpenModel
+
+[[autodoc]] SolarOpenModel
+    - forward
+
+## SolarOpenForCausalLM
+
+[[autodoc]] SolarOpenForCausalLM
+    - forward