---
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:
- enelpol/rag-mini-bioasq
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-v1

**Organization:** KRLabsOrg  

**Github:** https://github.com/KRLabsOrg/LettuceDetect

**Ettin encoders:** https://arxiv.org/pdf/2507.11412

## Overview

The 68M Ettin variant provides the highest accuracy among TinyLettuce models while remaining CPU‑friendly. It detects unsupported spans in answers given context and is optimized for low‑cost deployment and fine‑tuning.

Trained on our synthetic dataset (mixed with RAGTruth), this 68M variant reaches 92.64% F1 on the held‑out synthetic test set, proving the effectiveness of our domain‑specific hallucination data generation pipeline.

## 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

- Synthetic (train): ~1,500 hallucinated samples (≈3,000 incl. 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: 3–6
- 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 |
| 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-68m-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}, 
}
```