Instructions to use kyujinpy/KOR-Orca-Platypus-13B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyujinpy/KOR-Orca-Platypus-13B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/KOR-Orca-Platypus-13B-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/KOR-Orca-Platypus-13B-v2")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/KOR-Orca-Platypus-13B-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyujinpy/KOR-Orca-Platypus-13B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/KOR-Orca-Platypus-13B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/KOR-Orca-Platypus-13B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyujinpy/KOR-Orca-Platypus-13B-v2

SGLang

How to use kyujinpy/KOR-Orca-Platypus-13B-v2 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/KOR-Orca-Platypus-13B-v2" \
    --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/KOR-Orca-Platypus-13B-v2",
		"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/KOR-Orca-Platypus-13B-v2" \
        --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/KOR-Orca-Platypus-13B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyujinpy/KOR-Orca-Platypus-13B-v2 with Docker Model Runner:
```
docker model run hf.co/kyujinpy/KOR-Orca-Platypus-13B-v2
```
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.

🐳KOR-Orca-Platypus-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 kyujinpy/KOR-OpenOrca-Platypus-v3(private! wait!).

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
[KOR-Orca-Platypus-13B🐳]	46.59	42.06	53.95	42.28	43.55	51.12
KOR-Orca-Platypus-13B🐳-v2	49.48	44.03	54.43	42.23	41.64	65.05

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

Implementation Code

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

repo = "kyujinpy/KOR-Orca-Platypus-13B-v2"
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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