Text Generation
Transformers
PyTorch
t5
text2text-generation
UBC
African
Chaeetah
DLNLP
text-generation-inference
Instructions to use UBC-NLP/cheetah-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/cheetah-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UBC-NLP/cheetah-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/cheetah-base") model = AutoModelForSeq2SeqLM.from_pretrained("UBC-NLP/cheetah-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use UBC-NLP/cheetah-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UBC-NLP/cheetah-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UBC-NLP/cheetah-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UBC-NLP/cheetah-base
- SGLang
How to use UBC-NLP/cheetah-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 "UBC-NLP/cheetah-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": "UBC-NLP/cheetah-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 "UBC-NLP/cheetah-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": "UBC-NLP/cheetah-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UBC-NLP/cheetah-base with Docker Model Runner:
docker model run hf.co/UBC-NLP/cheetah-base
- Xet hash:
- 91fe10c86f18dbc0023611a0ffbf1810c9df8038f38c14bf89fea049534d4340
- Size of remote file:
- 2.33 GB
- SHA256:
- d4305d45ade02dface7679be9a3f4435f453185e5471cf98a9b44d142e99d995
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