Instructions to use Changgil/K2S3-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Changgil/K2S3-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Changgil/K2S3-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Changgil/K2S3-v0.1")
model = AutoModelForCausalLM.from_pretrained("Changgil/K2S3-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Changgil/K2S3-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Changgil/K2S3-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Changgil/K2S3-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Changgil/K2S3-v0.1

SGLang

How to use Changgil/K2S3-v0.1 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 "Changgil/K2S3-v0.1" \
    --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": "Changgil/K2S3-v0.1",
		"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 "Changgil/K2S3-v0.1" \
        --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": "Changgil/K2S3-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Changgil/K2S3-v0.1 with Docker Model Runner:
```
docker model run hf.co/Changgil/K2S3-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Developed by :

K2S3

Model Number:

K2S3-v0.1

Base Model Weight :

mistralai/Mistral-7B-v0.1

Model Description :

The K2S3 v0.1 model utilizes mistral weights, having undergone depth up scaling to double its size, and has been enhanced with the addition of Korean vocabulary and merges to the tokenizer.
K2S3 v0.1 모델은 mistral weight를 활용하였으며, depth up scaling을 통해 모델의 크기를 2배로 확장하였습니다. 또한, 토크나이저에는 한글 vocab과 merges를 추가하여 한국어 처리 능력을 강화하였습니다.

Training Data

The training data for this model includes alpaca-gpt4-data, and samples from The OpenOrca Dataset.
이 모델의 훈련 데이터에는 alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다.

Training Method

This model was trained on an enhanced version of the base model that underwent depth up scaling by K2S3, using a full parameter tuning method with SFT (Supervised Fine-Tuning).
이 모델은 K2S3에서 depth up scaling을 통해 확장한 버전의 기반 모델을 사용하여 SFT(Supervised Fine-Tuning)를 사용한 전체 파라미터 조정 방법으로 훈련되었습니다.

Hardware

Hardware: Utilized two A100 (80G*2EA) GPUs for training.
Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp.
이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.

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Safetensors

Model size

14B params

Tensor type

F16

Model tree for Changgil/K2S3-v0.1

Quantizations

3 models