metadata
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
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
@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}
}