Instructions to use OLAIR/ko-r1-14b-v2.0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OLAIR/ko-r1-14b-v2.0.3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OLAIR/ko-r1-14b-v2.0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OLAIR/ko-r1-14b-v2.0.3")
model = AutoModelForCausalLM.from_pretrained("OLAIR/ko-r1-14b-v2.0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OLAIR/ko-r1-14b-v2.0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OLAIR/ko-r1-14b-v2.0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OLAIR/ko-r1-14b-v2.0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OLAIR/ko-r1-14b-v2.0.3

SGLang

How to use OLAIR/ko-r1-14b-v2.0.3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OLAIR/ko-r1-14b-v2.0.3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OLAIR/ko-r1-14b-v2.0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OLAIR/ko-r1-14b-v2.0.3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OLAIR/ko-r1-14b-v2.0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OLAIR/ko-r1-14b-v2.0.3 with Docker Model Runner:
```
docker model run hf.co/OLAIR/ko-r1-14b-v2.0.3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

1. Overview

Model Name: OLAIR/ko-r1-14b-v2.0.3
Model Type: Large Language Model (LLM) for Korean language understanding and reasoning
Version: 2.0.3

This model is designed to provide Korean language capabilities with a focus on reasoning tasks. It is a scaled-up version of OLAIR/ko-r1-7b-v2.0.3 for experimental purposes.

2. Benchmark Performance

The model's performance has been evaluated using the HAE-RAE Reasoning Challenge (HRC), which measures reasoning abilities across five domains.

Model	Chemistry	Math	Physics	Physics Word Puzzles	Puzzles	Average
o1-2024-12-17	57.14	78.18	77.78	80.00	84.62	75.54
o3-mini-high	57.14	81.82	77.78	70.00	69.23	71.19
o3-mini-2025-01-31	50.00	80.00	70.37	50.00	76.92	65.46
o1-mini-2024-09-12	42.86	56.36	70.37	60.00	15.38	48.99
Deepseek-R1	50.00	54.55	62.96	70.00	7.69	49.04
gpt-4o-2024-11-20	35.71	32.73	51.85	50.00	53.85	44.83
Ko-R1-14B-v2.0.3	28.57	50.9	48.14	30.00	30.77	37.68
Exaone-3.5-32B-Instruct	21.43	30.91	25.93	50.00	38.46	33.35
Qwen2.5-72B-Instruct	35.71	30.91	51.85	20.00	23.08	32.31
Ko-R1-7B-v2.0.3	7.14	61.82	40.74	40.00	0.00	29.94
Ko-R1-7B-v1	7.14	63.64	37.04	40.00	0.00	29.56
gpt-4o-mini-2024-07-18	21.43	29.09	37.04	50.00	0.00	27.51
UNIVA-Bllossom_DeepSeek-llama3.1-Bllossom-8B	28.57	16.36	33.33	10.00	15.38	20.73

Comparison of Average Scores Ko-R1-14B-v2.0.3 outperforms non-reasoning models of way bigger size like Exaone-3.5-32B-Instruct, Qwen2.5-72B-Instruct, and gpt-4o-mini-2024-07-18.

Training Rewards Even when trained with the same datasets, bigger models just learn MORE.

3. Limitations

The model is still vulnerable to Korean-related inputs, leading to endless loops of thinking. We are working to fix it.

ETC

How to Cite

To be added

Contact

spthsrbwls123@yonsei.ac.kr

Downloads last month: 10

Safetensors

Model size

15B params

Tensor type

F32

Model tree for OLAIR/ko-r1-14b-v2.0.3

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

Finetuned

(84)

this model

Finetunes

1 model

Quantizations

3 models

OLAIR
/

ko-r1-14b-v2.0.3

1. Overview

2. Benchmark Performance

3. Limitations

ETC

Model tree for OLAIR/ko-r1-14b-v2.0.3

Dataset used to train OLAIR/ko-r1-14b-v2.0.3