Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
decision-model
system-one
biomedical
clinical-trials
pharmacology
pharmacovigilance
llm-as-a-judge
hallucination-detection
calibration
jev
tev1
typed-decisions
calibrated-probabilities
Eval Results (legacy)
Instructions to use lighteternal/biodecision-tev1-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lighteternal/biodecision-tev1-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lighteternal/biodecision-tev1-4b")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("lighteternal/biodecision-tev1-4b") model = AutoModelForMultimodalLM.from_pretrained("lighteternal/biodecision-tev1-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Benchmarking question
#1
by Chungulus - opened
Could you benchmark Jev as well and add it to your graphs or tables?
Hi! Yes, in fact I trained a better version and benchmarked both against Jev. Results are here:
https://huggingface.co/lighteternal/biodecision-v2-4b