Instructions to use ncbi/MedCPT-Query-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncbi/MedCPT-Query-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ncbi/MedCPT-Query-Encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Query-Encoder") model = AutoModel.from_pretrained("ncbi/MedCPT-Query-Encoder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ncbi/MedCPT-Query-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ncbi/MedCPT-Query-Encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://ncbi/MedCPT-Query-Encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ncbi/MedCPT-Query-Encoder/resolve/main/model.safetensors
438 MB
- Xet hash:
- 22c780ede51ea32262a56b47151e5d160915883ceab61ed22a57b2708fe20fd3
- Size of remote file:
- 438 MB
- SHA256:
- 19d78c0d5eaee2f81e6c47c5425bbadcc0c6af016cbb5da4a000d64e59d6e342
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.