Text Classification
Transformers
Safetensors
English
Chinese
qwen3_5
image-text-to-text
decision-model
system-one
biomedical
clinical-trials
pharmacovigilance
regulatory
genomics
llm-as-a-judge
calibration
jev
typed-decisions
calibrated-probabilities
Eval Results (legacy)
Instructions to use lighteternal/biodecision-v2-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lighteternal/biodecision-v2-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lighteternal/biodecision-v2-4b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("lighteternal/biodecision-v2-4b") model = AutoModelForMultimodalLM.from_pretrained("lighteternal/biodecision-v2-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download confidence.png from lighteternal/biodecision-v2-4b: direct link, hf CLI and curl.
- Browser
- Download file 157 kB
-
https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/confidence.png
- Command line
-
hf download hf://lighteternal/biodecision-v2-4b/confidence.png
-
curl -L -o confidence.png https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/confidence.png
157 kB

- Xet hash:
- b1993c3c7ac07e7779feb461ef68b6654a5fb807c259a7c4d22154ca9d6a7057
- Size of remote file:
- 157 kB
- SHA256:
- dda850a0504f0320fbc5f169ab6fba4c97ad2fc11ac37c07aa9456a43df4e27e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.