Instructions to use instructkr/ko-wand-136M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use instructkr/ko-wand-136M with Transformers:

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

pipe = pipeline("text-generation", model="instructkr/ko-wand-136M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("instructkr/ko-wand-136M")
model = AutoModelForCausalLM.from_pretrained("instructkr/ko-wand-136M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use instructkr/ko-wand-136M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "instructkr/ko-wand-136M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "instructkr/ko-wand-136M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/instructkr/ko-wand-136M

SGLang

How to use instructkr/ko-wand-136M 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 "instructkr/ko-wand-136M" \
    --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": "instructkr/ko-wand-136M",
		"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 "instructkr/ko-wand-136M" \
        --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": "instructkr/ko-wand-136M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use instructkr/ko-wand-136M with Docker Model Runner:
```
docker model run hf.co/instructkr/ko-wand-136M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ko-wand-136M

ko-wand-136M는 insturctkr에서 사전학습한 SLM입니다.

Model Description

maywell/korean_textbooks와 한국어 말뭉치를 이용하여 사전학습 되었습니다.

Model Info

미스트랄 아키텍쳐를 기반으로 완전히 랜덤 가중치를 시작으로 사전학습 된 모델입니다. Instruction 튜닝되지 않았습니다.

Training Details

Batch Size	Token Seen	lr
1024	2.5B	2e-3 (cosine)

License

apache-2.0

Downloads last month: 956

Model tree for instructkr/ko-wand-136M

Quantizations

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