huggingface--transformers/docs/source/en/model_doc/solar_open.md

This model was published in HF papers on 2026-01-11 and contributed to Hugging Face Transformers on 2026-01-22.

SolarOpen

Overview

The SolarOpen model was proposed in Solar Open Technical Report 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

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 from Upstage.

SolarOpenConfig

autodoc SolarOpenConfig

SolarOpenModel

autodoc SolarOpenModel - forward

SolarOpenForCausalLM

autodoc SolarOpenForCausalLM - forward