Instructions to use kyujinpy/Korean-OpenOrca-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyujinpy/Korean-OpenOrca-13B with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/Korean-OpenOrca-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/Korean-OpenOrca-13B")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/Korean-OpenOrca-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kyujinpy/Korean-OpenOrca-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/Korean-OpenOrca-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/Korean-OpenOrca-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyujinpy/Korean-OpenOrca-13B

SGLang

How to use kyujinpy/Korean-OpenOrca-13B 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 "kyujinpy/Korean-OpenOrca-13B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/Korean-OpenOrca-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "kyujinpy/Korean-OpenOrca-13B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/Korean-OpenOrca-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyujinpy/Korean-OpenOrca-13B with Docker Model Runner:
```
docker model run hf.co/kyujinpy/Korean-OpenOrca-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다
The license is cc-by-nc-sa-4.0.

🐳Korean-OpenOrca-13B🐳

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

Repo Link
Github Korean-OpenOrca: 🐳Korean-OpenOrca🐳

Base Model hyunseoki/ko-en-llama2-13b

Training Dataset
I use OpenOrca-KO.
Using DeepL, translate about OpenOrca.

I use A100 GPU 40GB and COLAB, when trianing.

Model Benchmark

KO-LLM leaderboard

Follow up as Open KO-LLM LeaderBoard.

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
Korean-OpenOrca-13B(ours🐳)	47.85	43.09	54.13	40.24	45.22	56.57
KoT-Platypus2-13B	49.55	43.69	53.05	42.29	43.34	65.38
KO-Platypus2-13B	47.90	44.20	54.31	42.47	44.41	54.11
hyunseoki/ko-en-llama2-13b	46.68	42.15	54.23	38.90	40.74	57.39
MarkrAI/kyujin-CoTy-platypus-ko-12.8b	46.44	34.98	49.11	25.68	37.59	84.86

Compare with Top 4 SOTA models. (update: 10/09)

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/Korean-OpenOrca-13B"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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