Instructions to use kyujinpy/Kosy-Platypus2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyujinpy/Kosy-Platypus2-13B with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/Kosy-Platypus2-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/Kosy-Platypus2-13B")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/Kosy-Platypus2-13B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyujinpy/Kosy-Platypus2-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/Kosy-Platypus2-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/Kosy-Platypus2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kyujinpy/Kosy-Platypus2-13B

SGLang

How to use kyujinpy/Kosy-Platypus2-13B 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/Kosy-Platypus2-13B" \
    --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/Kosy-Platypus2-13B",
		"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/Kosy-Platypus2-13B" \
        --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/Kosy-Platypus2-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyujinpy/Kosy-Platypus2-13B with Docker Model Runner:
```
docker model run hf.co/kyujinpy/Kosy-Platypus2-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kosy🍵llama

Model Details

Model Developers Kyujin Han (kyujinpy)

Model Description
NEFTune method를 활용하여 훈련한 Ko-platypus2 new version!
(Noisy + KO + llama = Kosy🍵llama)

Repo Link
Github KoNEFTune: Kosy🍵llama
If you visit our github, you can easily apply Random_noisy_embedding_fine-tuning!!

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

Training Dataset
Version of combined dataset: kyujinpy/KOpen-platypus
I use A100 GPU 40GB and COLAB, when trianing.

Model comparisons

KO-LLM leaderboard

NEFT comparisons

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
Ko-Platypus2-13B	45.60	44.20	54.31	42.47	44.41	42.62
*NEFT(🍵kosy)+MLP-v1	43.64	43.94	53.88	42.68	43.46	34.24
*NEFT(🍵kosy)+MLP-v2	45.45	44.20	54.56	42.60	42.68	42.98
*NEFT(🍵kosy)+MLP-v3	46.31	43.34	54.54	43.38	44.11	46.16
NEFT(🍵kosy)+Attention	44.92	42.92	54.48	42.99	43.00	41.20
NEFT(🍵kosy)	45.08	43.09	53.61	41.06	43.47	43.21

*Different Hyperparameters such that learning_rate, batch_size, epoch, etc...

Implementation Code

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

repo = "kyujinpy/Koisy-Platypus2-13B"
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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Safetensors

Model size

13B params

Tensor type

F16

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Quantizations

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Kosy-Platypus2-13B