Zero-Shot Classification
Transformers
Safetensors
GGUF
English
unknown
decision-model
system-one
falcondec
lightdec_v2
calibrated-decisions
multiple-choice
intent-classification
customer-support
natural-language-inference
code
guardrails
agents
selective-prediction
falconsai
model-surgeon
attested-lineage
Instructions to use Falconsai/LightDec_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/LightDec_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Falconsai/LightDec_V2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Falconsai/LightDec_V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download surgery_log.json from Falconsai/LightDec_V2: direct link, hf CLI and curl.
- Browser
- Download file 315 Bytes
-
https://huggingface.co/Falconsai/LightDec_V2/resolve/main/surgery_log.json
- Command line
-
hf download hf://Falconsai/LightDec_V2/surgery_log.json
-
curl -L -o surgery_log.json https://huggingface.co/Falconsai/LightDec_V2/resolve/main/surgery_log.json
315 Bytes
| { | |
| "copyright": "\u00a9 2026 FALCONS.AI", | |
| "log": [ | |
| { | |
| "op": "load", | |
| "detail": "hub:Falconsai/LightDec_V2/model.safetensors (319.3 MB, safetensors)", | |
| "t": 1790598780.0768738, | |
| "vitals": { | |
| "params": 159654157, | |
| "tensors": 168, | |
| "bytes": 319308314 | |
| } | |
| } | |
| ] | |
| } |