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

Libraries

How to use kyujinpy/ko-platypus-kiwi-13B with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/ko-platypus-kiwi-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/ko-platypus-kiwi-13B")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/ko-platypus-kiwi-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyujinpy/ko-platypus-kiwi-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/ko-platypus-kiwi-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/ko-platypus-kiwi-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyujinpy/ko-platypus-kiwi-13B

SGLang

How to use kyujinpy/ko-platypus-kiwi-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/ko-platypus-kiwi-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/ko-platypus-kiwi-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/ko-platypus-kiwi-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/ko-platypus-kiwi-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyujinpy/ko-platypus-kiwi-13B with Docker Model Runner:
```
docker model run hf.co/kyujinpy/ko-platypus-kiwi-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.

KOR-Orca-Platypus-kiwi🥝

Model Details

Model Developers Kyujin Han (kyujinpy)

Model Architecture
ko-platypus-kiwi-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

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

Training Dataset
I used kyujinpy/KOR-Orca-Platypus-kiwi.

Model comparisons

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
ko-platypus-kiwi-13B🥝	48.97	42.41	54.29	41.98	40.05	66.12

Implementation Code

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

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

Downloads last month: 4

Model tree for kyujinpy/ko-platypus-kiwi-13B

Quantizations

2 models

kyujinpy
/

ko-platypus-kiwi-13B

KOR-Orca-Platypus-kiwi🥝

Model Details

Model comparisons

Implementation Code

Model tree for kyujinpy/ko-platypus-kiwi-13B

Dataset used to train kyujinpy/ko-platypus-kiwi-13B

Spaces using kyujinpy/ko-platypus-kiwi-13B 9