KRLabsOrg
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tinylettuce-ettin-68m-en

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 library_name: transformers
-tags: []
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 ---
+license: mit
+language:
+- en
+base_model:
+- jhu-clsp/ettin-encoder-68m
+pipeline_tag: token-classification
+tags:
+- token classification
+- hallucination detection
+- retrieval-augmented generation
+- transformers
+- ettin
+- lightweight
+datasets:
+- ragtruth
+- KRLabsOrg/rag-bioasq-lettucedetect
 library_name: transformers
 ---
+# TinyLettuce (Ettin-68M): Efficient Hallucination Detection
+<p align="center">
+  <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/dev/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/>
+</p>
+**Model Name:** tinylettuce-ettin-68m-en
+**Organization:** KRLabsOrg
+**Github:** https://github.com/KRLabsOrg/LettuceDetect
+**Ettin encoders:** https://arxiv.org/pdf/2507.11412
+## Overview
+TinyLettuce is a lightweight token‑classification model that flags unsupported spans in answers given context (span aggregation performed downstream). Built on the 68M 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 68M Ettin encoder and a token‑classification head. Highest accuracy among TinyLettuce sizes, works great given it's size (74.97% vs 76.07 LettuceDetect-ModernBERT-base); optimized for efficient CPU inference.
+## Model Details
+- Architecture: Ettin encoder (68M) + token‑classification head
+- Task: token classification (0 = supported, 1 = hallucinated)
+- Input: [CLS] context [SEP] question [SEP] answer [SEP], up to 4096 tokens
+- Language: English; License: MIT
+## Training Data
+- RAGTruth (English), span‑level labels; no synthetic data mixed
+## Training Procedure
+- Tokenizer: AutoTokenizer; DataCollatorForTokenClassification; label pad −100
+- Max length: 4096; batch size: 8; epochs: 3–6
+- Optimizer: AdamW (lr 1e‑5, weight_decay 0.01)
+- Hardware: Single A100 80GB
+## Results (RAGTruth)
+This model is designed primarily for fine-tuning on smaller, domain-specific samples, rather than for general use.
+Performs well on the RAGTruth benchmark, coming close to our LettuceDetect-base (150m ModernBERT) model.
+| Model | Parameters | F1 (%) |
+|-------|------------|--------|
+| **TinyLettuce-68M** | 68M | **74.97** |
+| LettuceDetect-base (ModernBERT) | 150M | 76.07 |
+| LettuceDetect-large (ModernBERT) | 395M | 79.22 |
+| Llama-2-13B (RAGTruth FT) | 13B | 78.70 |
+## Usage
+First install lettucedetect:
+```bash
+pip install lettucedetect
+```
+Then use it:
+```python
+from lettucedetect.models.inference import HallucinationDetector
+detector = HallucinationDetector(
+    method="transformer",
+    model_path="KRLabsOrg/tinylettuce-ettin-68m-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"}]
+```
+## Citing
+If you use the model or the tool, please cite the following paper:
+```bibtex
+@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},
+}
+```