Instructions to use Databook/SmolClassifierMed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Databook/SmolClassifierMed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Databook/SmolClassifierMed")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Databook/SmolClassifierMed") model = AutoModel.from_pretrained("Databook/SmolClassifierMed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from Databook/SmolClassifierMed: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/Databook/SmolClassifierMed/resolve/main/model.safetensors
- Command line
-
hf download hf://Databook/SmolClassifierMed/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Databook/SmolClassifierMed/resolve/main/model.safetensors
1.45 GB
- Xet hash:
- 58a5d45ebcd7519e4fe50bc73543ed084f82d1a454d078aeaa537163bf066bc1
- Size of remote file:
- 1.45 GB
- SHA256:
- 225b655ce5f874c5abb4c38307ed461a3339a3260147a0e4fe0716f73f1c31c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.