How to use from
SGLangUse 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 "lbox/lcube-base" \
--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": "lbox/lcube-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
How to use
import transformers
model = transformers.GPT2LMHeadModel.from_pretrained("lbox/lcube-base")
tokenizer = transformers.AutoTokenizer.from_pretrained(
"lbox/lcube-base",
bos_token="[BOS]",
unk_token="[UNK]",
pad_token="[PAD]",
mask_token="[MASK]",
)
text = "ํผ๊ณ ์ธ์ ๋ถ์์ง์ ์๋ ์ปคํผ์์์, ํผํด์ B์ผ๋ก๋ถํฐ"
model_inputs = tokenizer(text,
max_length=1024,
padding=True,
truncation=True,
return_tensors='pt')
out = model.generate(
model_inputs["input_ids"],
max_new_tokens=150,
pad_token_id=tokenizer.pad_token_id,
use_cache=True,
repetition_penalty=1.2,
top_k=5,
top_p=0.9,
temperature=1,
num_beams=2,
)
tokenizer.batch_decode(out)
For more information please visit https://github.com/lbox-kr/lbox_open.
Licensing Information
Copyright 2022-present LBox Co. Ltd.
Licensed under the CC BY-NC-ND 4.0
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "lbox/lcube-base" \ --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": "lbox/lcube-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'