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KRLabsOrg/tinylettuce-ettin-32m-en
tinylettuce-ettin-32m-en is a token classification model from KRLabsOrg. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
<p align="center" <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/dev/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/ </p
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From the Hugging Face model README
Model Name: tinylettuce-ettin-32m-en
Organization: KRLabsOrg
Github: https://github.com/KRLabsOrg/LettuceDetect
Ettin encoders: https://arxiv.org/pdf/2507.11412
TinyLettuce is a lightweight token‑classification model that flags unsupported spans in answers given context (span aggregation performed downstream). Built on the 32M Ettin encoder, it targets real‑time CPU inference and low‑cost domain fine‑tuning. This variant is trained only on our synthetic data and RAGTruth dataset for hallucination detection, using the 32M Ettin encoder and a token‑classification head. Balanced accuracy and speed; CPU‑friendly deployment.
This model is designed primarily for fine-tuning on smaller, domain-specific samples, rather than for general use (though it still performs notably on Ragtruth, 72% vs 76% (our ModernBERT based model)).
| Model | Parameters | F1 (%) |
|---|---|---|
| TinyLettuce-32M | 32M | 72.15 |
| LettuceDetect-base (ModernBERT) | 150M | 76.07 |
| LettuceDetect-large (ModernBERT) | 395M | 79.22 |
| Llama-2-13B (RAGTruth FT) | 13B | 78.70 |
First install lettucedetect:
pip install lettucedetect
Then use it:
from lettucedetect.models.inference import HallucinationDetector
detector = HallucinationDetector(
method="transformer",
model_path="KRLabsOrg/tinylettuce-ettin-32m-en",
)
spans = detector.predict(
context=[
"Ibuprofen is an NSAID that reduces inflammation and pain. The typical adult dose is 400-600mg every 6-8 hours, not exceeding 2400mg daily."
],
question="What is the maximum daily dose of ibuprofen?",
answer="The maximum daily dose of ibuprofen for adults is 3200mg.",
output_format="spans",
)
print(spans)
# Output: [{"start": 51, "end": 57, "text": "3200mg"}]
If you use the model or the tool, please cite the following paper:
@misc{Kovacs:2025,
title={LettuceDetect: A Hallucination Detection Framework for RAG Applications},
author={Ádám Kovács and Gábor Recski},
year={2025},
eprint={2502.17125},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.17125},
}