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 data.png from lighteternal/biodecision-v2-4b: direct link, hf CLI and curl.
- Browser
- Download file 194 kB
-
https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/data.png
- Command line
-
hf download hf://lighteternal/biodecision-v2-4b/data.png
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curl -L -o data.png https://huggingface.co/lighteternal/biodecision-v2-4b/resolve/main/data.png
194 kB

- Xet hash:
- a6dd64427f5addbabc41191f9804571f12e0c4f4e404ce060dc92e79f467cf33
- Size of remote file:
- 194 kB
- SHA256:
- e2bd3b714c7731cb0f3f882485e16e1900e3821fca3ebe9f6c03f2b69fa2550b
·
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