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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
| ## How to use | |
| ```python | |
| 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 |