71 lines
1.8 KiB
Markdown
71 lines
1.8 KiB
Markdown
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---
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license: cc-by-4.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-8B
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pipeline_tag: text-generation
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---
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# 🧬 Thoth
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**Thoth** is a lightweight version of Thoth, designed for **efficient and scalable biological protocol generation** while retaining strong scientific reasoning ability.
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- 📄 **Paper**: *Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism* (ICLR 2026)
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- 🔗 **GitHub**: https://github.com/manglu097/Thoth
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- 🤗 **Dataset**: https://huggingface.co/datasets/manglu3935/SciRecipe
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---
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## 🔍 Model Overview
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- **Base model**: Qwen3-8B
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- **Parameters**: 8B
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- **GPU memory**: ~16GB
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- **Primary task**: Biological experimental protocol generation
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Thoth 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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---
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## 🧠 Output Format
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```
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<think> reasoning and planning </think>
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<key> structured machine-readable steps </key>
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<orc> natural language protocol </orc>
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<note> optional safety notes </note>
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```
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---
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## 🚀 Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("manglu3935/Thoth")
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model = AutoModelForCausalLM.from_pretrained("manglu3935/Thoth")
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```
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---
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## ⚠️ Intended Use
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For fast scientific reasoning experiments and scalable research deployment.
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Generated protocols must be reviewed by qualified experts prior to laboratory execution.
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---
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## 📖 Citation
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```bibtex
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@article{sun2025unleashing,
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title={Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism},
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author={Sun, Haoran and Jiang, Yankai and Tang, Zhenyu and others},
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journal={arXiv preprint arXiv:2510.15600},
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year={2025}
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}
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```
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