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README.md

@@ -5,4 +5,66 @@ language:
 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
+
+```python
+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
+
+```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}
+}
+```