Instructions to use kyujinpy/Korean-OpenOrca-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyujinpy/Korean-OpenOrca-v3 with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/Korean-OpenOrca-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/Korean-OpenOrca-v3")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/Korean-OpenOrca-v3")

Inference
Local Apps Settings

vLLM

How to use kyujinpy/Korean-OpenOrca-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/Korean-OpenOrca-v3"
# 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-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyujinpy/Korean-OpenOrca-v3

SGLang

How to use kyujinpy/Korean-OpenOrca-v3 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-v3" \
    --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-v3",
		"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-v3" \
        --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-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyujinpy/Korean-OpenOrca-v3 with Docker Model Runner:
```
docker model run hf.co/kyujinpy/Korean-OpenOrca-v3
```
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-v2🐳

Model Details

Model Developers Kyujin Han (kyujinpy)

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-v3.
Using DeepL, translate about OpenOrca.

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

Model comparisons

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
[Korean-OpenOrca-13B🐳]	48.79	43.09	54.13	40.24	45.22	61.28
[Korean-OpenOrca-13B-v2🐳]	48.17	43.17	54.51	42.90	41.82	58.44
Korean-OpenOrca-13B-v3🐳	48.86	43.77	54.30	41.79	43.85	60.57

Implementation Code

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

repo = "kyujinpy/Korean-OpenOrca-13B-v3"
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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Safetensors

Model size

13B params

Tensor type

F16

Model tree for kyujinpy/Korean-OpenOrca-v3

Quantizations

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Korean-OpenOrca-v3

🐳Korean-OpenOrca-13B-v2🐳

Model Details

Model comparisons

Implementation Code

Model tree for kyujinpy/Korean-OpenOrca-v3

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