metadata
license: cc-by-4.0
language:
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
🧬 Thoth
Thoth 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: Qwen3-8B
- Parameters: 8B
- GPU memory: ~16GB
- Primary task: Biological experimental protocol generation
Thoth is trained with the same Sketch-and-Fill paradigm and SCORE reward mechanism as Thoth, offering a strong performance–efficiency trade-off.
🧠 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 fast scientific reasoning experiments and scalable research deployment.
Generated protocols must be reviewed by qualified experts prior to laboratory execution.
📖 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}
}