How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lbox/lcube-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lbox/lcube-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/lbox/lcube-base
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€ ๋ถˆ์ƒ์ง€์— ์žˆ๋Š” ์ปคํ”ผ์ˆ์—์„œ, ํ”ผํ•ด์ž B์œผ๋กœ๋ถ€ํ„ฐ"
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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