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KORMo-Team/
KORMo-10B-base

Name: KORMo-10B-base
Availability: InStock

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🚀 Update News

2025-10-13: Official release of KORMo-10B-base (Be aware that it's not an SFT model!!).

💡 About KORMo

KORMo-10B is a 10.8B parameter fully open LLM capable of handling both Korean and English.
The model, training code, and training data are all fully open, allowing anyone to reproduce and extend them.

Model Size: 10.8B parameters
Languages: Korean / English
Training Data: Synthetic data + public datasets (approximately 3T tokens)
License: Apache 2.0

The First Fully Open-Source LLM from a Non-English Region

KORMo was created with a public-interest mission: to make world-class language models accessible to everyone.
Our goal is to empower anyone to build and advance their own large language models at a global standard.

Key Features:

1. A 10B-parameter Korean–English reasoning model trained entirely from scratch.
2. 100% open resources — including all training data, code, intermediate checkpoints, and tutorials — allowing anyone to reproduce and extend a near-SOTA model on their own.
3. 3 trillion tokens of training data released publicly, featuring never-before-shared, high-quality full-cycle Korean datasets (for pretraining, post-training, general, reasoning, and reinforcement learning).
4. A collaborative effort by eight master’s students at the KAIST Graduate School of Culture Technology (MLP Lab), documented in a 45-page research paper.

If you’ve ever used a Korean language model that performs well on benchmarks but feels strange in real use, or if fine-tuning only made it worse, you’re not alone.

KORMo solves these problems head-on.
By releasing every intermediate model and post-training dataset, we give users the freedom to build on the base model with their own data, customizing and fine-tuning it in any direction they want.

👉 "If you want a great Korean language model, now you can build it yourself. It even works with free Colab GPUs!" 🤗

🔗 Links

📖 Technical Report: 👉 Arxive
🤗 Hugging Face: 👉 Model Download
💻 GitHub Repository: 👉 Training and Inference Code
🔉 Tutorial: 👉 Instruction Tuning over google colab 👉 Youtube Tutorial

📈 Benchmark Performance

📊 Quantitative Evaluation

Benchmark	KORMo-10B	smolLM3-3B	olmo2-7B	olmo2-13B	kanana1.5-8B	qwen3-8B	llama3.1-8B	gemma3-4B	gemma3-12B
🇺🇸 English Benchmarks
arc_challenge	58.96	55.55	59.13	61.01	56.48	63.82	54.61	53.58	63.82
arc_easy	85.48	83.21	85.06	86.57	82.74	87.50	84.01	82.83	87.37
boolq	83.46	82.17	84.50	86.48	84.53	87.71	81.87	80.70	86.61
copa	93.00	91.00	92.00	93.00	88.00	92.00	93.00	89.00	95.00
gpqa_main	30.13	26.79	26.34	29.24	29.24	30.13	23.44	30.13	35.71
hellaswag	60.25	56.78	61.52	65.02	59.93	59.54	60.96	57.56	63.67
mmlu	67.96	61.37	62.81	66.85	63.73	76.95	65.03	59.60	73.58
mmlu_global	63.44	57.52	59.88	63.99	60.21	75.05	61.30	57.23	70.23
mmlu_pro	40.18	34.94	27.29	32.50	34.93	56.58	36.23	27.79	37.07
mmlu_redux	69.00	62.95	63.53	68.37	65.88	78.19	65.86	60.86	75.25
openbookqa	39.00	36.40	39.00	39.60	36.80	39.20	39.00	37.00	40.20
piqa	81.12	78.45	80.79	82.64	80.30	79.05	80.90	79.49	82.59
social_iqa	52.81	50.72	55.89	57.57	57.01	56.96	53.12	51.84	56.45
English Avg.	63.45	59.83	61.36	64.06	61.52	67.90	61.49	59.05	66.73
🇰🇷 Korean Benchmarks
click	55.29	46.97	37.79	41.80	62.76	60.70	49.22	49.62	62.21
csatqa	38.00	26.67	19.33	24.67	44.67	52.00	28.67	28.67	31.33
haerae	68.29	55.82	31.62	37.58	80.75	67.19	53.25	60.68	74.34
k2_eval	84.89	75.23	49.54	63.43	84.72	84.72	76.62	76.39	85.42
kobest	75.05	69.13	57.27	59.02	81.93	80.05	70.55	69.33	77.70
kobalt	22.86	15.86	11.43	13.14	26.29	26.57	17.43	15.57	23.86
kmmlu	46.48	38.52	33.05	31.24	48.86	56.93	40.75	39.84	51.60
mmlu_global (ko)	55.16	44.15	34.00	36.95	52.65	61.95	46.34	46.33	59.68
kr_clinical_qa	77.32	53.97	48.33	46.22	65.84	80.00	63.54	60.00	77.22
Korean Avg.	58.15	47.37	35.82	39.34	60.94	63.35	49.60	49.60	60.37

📝 Qualitative Evaluation (LLM-as-a-Judge)

Benchmark	KORMo-10B	smolLM3-3B	olmo2-7B	olmo2-13B	kanana1.5-8B	qwen3-8B	llama3.1-8B	exaone3.5-8B	gemma3-12B
MT-Bench (EN)	8.32	7.15	7.32	7.64	8.45	8.70	6.32	8.15	8.70
KO-MT-Bench (KO)	8.54	-	-	-	8.02	8.16	4.27	8.13	8.51
LogicKor (KO)	8.96	-	-	-	8.94	8.63	6.45	9.20	8.46
Average	8.61	-	-	-	8.47	8.50	5.68	8.49	8.56

📦 Installation

bash

git clone https://github.com/MLP-Lab/KORMo-tutorial.git
cd KORMo-tutorial
bash setup/create_uv_venv.sh
source .venv_kormo/bin/activate

🚀 Inference Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "KORMo-Team/KORMo-10B-sft"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "What happens inside a black hole?"}
]

chat_prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=1024,
    )

response = tokenizer.decode(output_ids[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print("Assistant:", response)

🧠 Enabling Thinking Mode

If you want to enable the thinking mode, simply set enable_thinking=True: