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license: cc-by-4.0
---
# 🧬 Thoth-mini
**Thoth-mini** is a lightweight version of Thoth, designed for **efficient and scalable biological protocol generation** while retaining strong scientific reasoning ability.
- 📄 **Paper**: *Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism* (ICLR 2026)
- 🔗 **GitHub**: https://github.com/manglu097/Thoth
- 🤗 **Dataset**: https://huggingface.co/datasets/manglu3935/SciRecipe
---
## 🔍 Model Overview
- **Base model**: Qwen3-4B
- **Parameters**: 4B
- **GPU memory**: ~8GB
- **Primary task**: Biological experimental protocol generation
Thoth-mini is trained with the same **Sketch-and-Fill paradigm** and **SCORE reward mechanism** as Thoth, offering a strong performance–efficiency trade-off.
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## 🧠 Output Format
```
<think> reasoning and planning </think>
<key> structured machine-readable steps </key>
<orc> natural language protocol </orc>
<note> optional safety notes </note>
```
---
## 🚀 Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("manglu3935/Thoth-mini")
model = AutoModelForCausalLM.from_pretrained("manglu3935/Thoth-mini")
```
---
## ⚠️ Intended Use
For fast scientific reasoning experiments and scalable research deployment.
Generated protocols must be reviewed by qualified experts prior to laboratory execution.
---
## 📖 Citation
```bibtex
@article{sun2025unleashing,
title={Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism},
author={Sun, Haoran and Jiang, Yankai and Tang, Zhenyu and others},
journal={arXiv preprint arXiv:2510.15600},
year={2025}
}
```
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