Instructions to use lbox/lcube-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lbox/lcube-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lbox/lcube-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lbox/lcube-base") model = AutoModelForCausalLM.from_pretrained("lbox/lcube-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lbox/lcube-base with 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
- SGLang
How to use lbox/lcube-base 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 "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 }'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 "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 }' - Docker Model Runner
How to use lbox/lcube-base with Docker Model Runner:
docker model run hf.co/lbox/lcube-base
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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docker model run hf.co/lbox/lcube-base