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 latency.png from lighteternal/biodecision-v2-4b: direct link, hf CLI and curl.
- Browser
- Download file 142 kB
-
https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/latency.png
- Command line
-
hf download hf://lighteternal/biodecision-v2-4b/latency.png
-
curl -L -o latency.png https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/latency.png
142 kB

- Xet hash:
- 9bdbf71dced9cde6ac993c3fb7c66b2da9d7cc56cd1e601940eed521f8e4de09
- Size of remote file:
- 142 kB
- SHA256:
- 6371d786024352cc360950caed8f8392881e650d2cb6c790478fc31d501643a7
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