Instructions to use jimypbr/bart-large-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jimypbr/bart-large-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jimypbr/bart-large-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jimypbr/bart-large-test") model = AutoModelForSeq2SeqLM.from_pretrained("jimypbr/bart-large-test") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jimypbr/bart-large-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jimypbr/bart-large-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jimypbr/bart-large-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jimypbr/bart-large-test
- SGLang
How to use jimypbr/bart-large-test 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 "jimypbr/bart-large-test" \ --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": "jimypbr/bart-large-test", "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 "jimypbr/bart-large-test" \ --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": "jimypbr/bart-large-test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jimypbr/bart-large-test with Docker Model Runner:
docker model run hf.co/jimypbr/bart-large-test
- Xet hash:
- 1894e930e5ccddde523e35f7cc020a47b119f95fa746b3023d4b95fa1fefcf91
- Size of remote file:
- 813 MB
- SHA256:
- cce86369b4ba09fe233e530a14bef679006424f7c5b5cdc8335363ea51e4125c
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