Zero-Shot Classification
Transformers
Safetensors
GGUF
English
decision-model
system-one
falcondec
lightdec
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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/LightDec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Falconsai/LightDec")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Falconsai/LightDec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,159 Bytes
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"max_tok_per_opt": 24,
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"interact_layers": 2,
"interact_heads": 8,
"name": "LightDec",
"backbone": "jhu-clsp/ettin-encoder-150m",
"backbone_init": "pretrained",
"lineage": [
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"n_buckets": 4,
"version": "1.0.1",
"notebook_version": "V2",
"created": "2026-09-26T11:28:22.623723Z",
"temperature": [
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"weights": "model.safetensors"
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