EDT-Former SFT (Instruction Tuning) Data

Supervised fine-tuning corpus for EDT-Former Stage 2, as described in the ICLR 2026 paper:

Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding Zihao Jing, Qiuhao Zeng, Ruiyi Fang, Yan Sun, Boyu Wang, Pingzhao Hu ICLR 2026 · Paper · Code

Dataset Description

This dataset is used to fine-tune the full EDT-Former model (encoder + Llama-3.1-8B-Instruct) on molecular instruction-following tasks. It is derived from the Mol-LLaMA-Instruct dataset, preprocessed into JSONL format with molecule graph features.

Data Files

Usage

from datasets import load_dataset

dataset = load_dataset("zihaojing/EDT-Former-sft-data")

Or point to it in the EDT-Former config:

# configs/stage2_dqw2d/data_config_preprocessed.yaml
use_preprocessed: true
preprocessed_data: zihaojing/EDT-Former-sft-data

Related Resources

Citation

@inproceedings{jing2026edtformer,
  title={Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding},
  author={Jing, Zihao and Zeng, Qiuhao and Fang, Ruiyi and Sun, Yan and Wang, Boyu and Hu, Pingzhao},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026}
}