Instructions to use Jay-Cho/KCOMP-BioASQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jay-Cho/KCOMP-BioASQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jay-Cho/KCOMP-BioASQ")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jay-Cho/KCOMP-BioASQ") model = AutoModelForCausalLM.from_pretrained("Jay-Cho/KCOMP-BioASQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jay-Cho/KCOMP-BioASQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jay-Cho/KCOMP-BioASQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jay-Cho/KCOMP-BioASQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jay-Cho/KCOMP-BioASQ
- SGLang
How to use Jay-Cho/KCOMP-BioASQ 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 "Jay-Cho/KCOMP-BioASQ" \ --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": "Jay-Cho/KCOMP-BioASQ", "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 "Jay-Cho/KCOMP-BioASQ" \ --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": "Jay-Cho/KCOMP-BioASQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jay-Cho/KCOMP-BioASQ with Docker Model Runner:
docker model run hf.co/Jay-Cho/KCOMP-BioASQ
|
Download README.md from Jay-Cho/KCOMP-BioASQ: direct link, hf CLI and curl.
- Browser
- Download file 586 Bytes
-
https://huggingface.co/Jay-Cho/KCOMP-BioASQ/resolve/main/README.md
- Command line
-
hf download hf://Jay-Cho/KCOMP-BioASQ/README.md
-
curl -L -o README.md https://huggingface.co/Jay-Cho/KCOMP-BioASQ/resolve/main/README.md
586 Bytes
| library_name: transformers | |
| tags: [] | |
| ## Citation [optional] | |
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> | |
| **BibTeX:** | |
| ``` | |
| @misc{cho2025kcompretrievalaugmentedmedicaldomain, | |
| title={K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor}, | |
| author={Jeonghun Cho and Gary Geunbae Lee}, | |
| year={2025}, | |
| eprint={2501.13567}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL}, | |
| url={https://arxiv.org/abs/2501.13567}, | |
| } | |
| ``` |