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tinylettuce-ettin-32m-en-bioasq

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 library_name: transformers
-tags: []
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 ---
+license: mit
+language:
+- en
+base_model:
+- jhu-clsp/ettin-encoder-32m
+pipeline_tag: token-classification
+tags:
+- token classification
+- hallucination detection
+- retrieval-augmented generation
+- transformers
+- ettin
+- lightweight
+datasets:
+- enelpol/rag-mini-bioasq
 library_name: transformers
 ---
+# TinyLettuce (Ettin-32M): Efficient Hallucination Detection
+<p align="center">
+  <img src="https://github.com/KRLabsOrg/LettuceDetect/blob/main/assets/tinytinylettuce.png?raw=true" alt="TinyLettuce" width="400"/>
+</p>
+**Model Name:** tinylettuce-ettin-32m-en-v1
+**Organization:** KRLabsOrg
+**Github:** https://github.com/KRLabsOrg/LettuceDetect
+**Ettin encoders:** https://arxiv.org/pdf/2507.11412
+## Overview
+TinyLettuce is a token‑classification model that flags unsupported spans in answers given context. The 32M Ettin variant balances accuracy and CPU‑side efficiency; it’s designed for low‑cost domain fine‑tuning on synthetic data.
+Trained on our synthetic dataset (mixed with RAGTruth), this 32M variant achieves 88.76% F1 on the held‑out synthetic test set (beating large-scale LLM judges like GPT-OSS-120b), proving the effectiveness of our domain‑specific hallucination data generation pipeline.
+## Model Details
+- Architecture: Ettin encoder (32M) + 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
+- Synthetic (train): ~1,500 hallucinated samples (≈3,000 with non‑hallucinated) from enelpol/rag-mini-bioasq; intensity 0.3.
+- Synthetic (test): 300 hallucinated samples (≈600 total) held out.
+## Training Procedure
+- Tokenizer: AutoTokenizer; DataCollatorForTokenClassification; label pad −100
+- Max length: 4096; batch size: 8; epochs: 3
+- Optimizer: AdamW (lr 1e‑5, weight_decay 0.01)
+- Hardware: Single A100 80GB
+## Results
+Synthetic (domain‑specific):
+| Model | Parameters | Precision (%) | Recall (%) | F1 (%) | Hardware |
+|-------|------------|---------------|------------|--------|----------|
+| TinyLettuce-17M | 17M | 84.56 | 98.21 | 90.87 | CPU |
+| **TinyLettuce-32M** | 32M | 80.36 | 99.10 | 88.76 | CPU |
+| TinyLettuce-68M | 68M | 89.54 | 95.96 | 92.64 | CPU |
+| GPT-5-mini | ~200B | 71.95 | 100.00 | 83.69 | API/GPU |
+| GPT-OSS-120B | 120B | 72.21 | 98.64 | 83.38 | GPU |
+| Qwen3-235B | 235B | 66.74 | 99.32 | 79.84 | GPU |
+## 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-32m-en-v1",
+)
+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},
+}
+```