README.md

---
license: cc-by-4.0
language:
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
---

# 🧬 Thoth

**Thoth** is a large language model for **biological experimental protocol generation**, designed to transform scientific knowledge into **accurate, logically ordered, and executable wet-lab procedures**.

- 📄 **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**: Qwen3-8B  
- **Parameters**: 8B  
- **Training data**: SciRecipe (12K+ expert-curated biological protocols across 27 subfields)  
- **Primary task**: End-to-end biological experimental protocol generation  

Thoth follows a **Sketch-and-Fill** reasoning paradigm and is optimized using a **Structured Component-based Reward (SCORE)** mechanism, enforcing step ordering, granularity control, and semantic consistency.

---

## 🧠 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")
model = AutoModelForCausalLM.from_pretrained("manglu3935/Thoth")
```

---

## ⚠️ Intended Use

For research on scientific reasoning and experimental protocol generation.  
Generated protocols must be reviewed by qualified domain experts before laboratory use.

---

## 📖 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}
}
```