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
Delete falcondec_config.json
Browse files- falcondec_config.json +0 -54
falcondec_config.json
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{
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"max_len": 512,
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"long_max_len": 2048,
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"long_opts_threshold": 24,
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"head_max_len": 192,
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"max_tok_per_opt": 24,
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"max_opts_single_pass": 96,
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"interact_layers": 2,
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"interact_heads": 8,
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"name": "FalconDec",
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"backbone": "jhu-clsp/ettin-encoder-150m",
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"backbone_init": "pretrained",
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"lineage": [
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"jhu-clsp/ettin-encoder-150m"
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],
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"parent_version": null,
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"special": {
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"cls": 50281,
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"sep": 50282,
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"mask": 50284,
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"pad": 50283
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},
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"defer_threshold": 0.7,
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"qtypes": {
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"choice": 0,
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"noul": 1,
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"score": 2
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},
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"n_buckets": 4,
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"version": "1.0.0",
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"notebook_version": "V2",
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"created": "2026-09-26T04:35:05.339042Z",
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"temperature": [
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[
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1.7067416906356812,
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1.3524072170257568,
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1.4660307168960571,
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1.3491928577423096
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],
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"weights": "model.safetensors"
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}
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