Instructions to use UCSC-VLAA/MedReason-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/MedReason-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="UCSC-VLAA/MedReason-Mistral")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/MedReason-Mistral", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- caf679326db54f99eb95bece770f30f1a65f7547779fa0f6c686eb69e2b1bbb3
- Size of remote file:
- 672 Bytes
- SHA256:
- 28677d22f2663bf1ca213034d3d3e2581731abb11418c9dbf414d4d9a79d36ac
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.