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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- molecules
+- chemistry
+- molecular-understanding
+- graph-llm
+- llama
+- instruction-tuning
+pipeline_tag: text-generation
+base_model: unsloth/Llama-3.1-8B-Instruct
+---
+
+# EDT-Former: Full Model (Stage 2)
+
+The full **EDT-Former** model (DQ-Former encoder + Llama-3.1-8B-Instruct), 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](https://www.arxiv.org/abs/2602.02742) · [Code](https://github.com/selmiss/DQ-Former)
+
+## Model Description
+
+EDT-Former aligns molecular graphs with a frozen LLM backbone (Llama-3.1-8B-Instruct) via a DQ-Former connector. Key properties:
+
+- **No LLM backbone fine-tuning** (only the embedding layer and connector are trained) — computationally efficient
+- **Entropy-guided dynamic token selection** preserves both local (substructural) and global molecular features
+- **State-of-the-art** on MoleculeQA, Mol-Instructions (forward reaction, retrosynthesis, reagent prediction, mol design, open QA), TDC, and MoleculeNet benchmarks
+
+This Stage 2 checkpoint (~16 GB) is the final instruction-tuned model ready for downstream molecular QA tasks.
+
+## Usage
+
+```python
+# 1. Clone the repo and set up the environment
+git clone https://github.com/selmiss/DQ-Former.git
+cd DQ-Former
+conda env create -f environment.yml
+conda activate edtformer
+
+# 2. Configure paths in local.env.sh
+cp env.sh local.env.sh
+# Edit local.env.sh: set BASE_DIR, DATA_DIR, CHECKPOINT_DIR
+source local.env.sh
+
+# 3. Download model
+from huggingface_hub import snapshot_download
+snapshot_download("zihaojing/DQFormer-model", local_dir="checkpoints/edt_former_s2_large/final_model")
+
+# 4. Run inference (example: forward reaction prediction)
+bash scripts/qa/mol_forward.sh
+```
+
+## Downstream Task Scripts
+
+All evaluation scripts are in `scripts/qa/`. Example tasks:
+
+| Task | Script |
+|------|--------|
+| Forward Reaction Prediction | `scripts/qa/mol_forward.sh` |
+| Retrosynthesis | `scripts/qa/retrosynthesis.sh` |
+| Reagent Prediction | `scripts/qa/reagent_prediction.sh` |
+| Molecule Design | `scripts/qa/mol_design.sh` |
+| Open-ended QA | `scripts/qa/open_question.sh` |
+
+## Training Details
+
+| Setting | Value |
+|---------|-------|
+| LLM backbone | Llama-3.1-8B-Instruct (frozen) |
+| Stage 1 encoder | [zihaojing/DQFormer-encoder](https://huggingface.co/zihaojing/DQFormer-encoder) |
+| Training data | [zihaojing/DQFormer-sft-data](https://huggingface.co/datasets/zihaojing/DQFormer-sft-data) |
+| Epochs | 2 |
+| Learning rate | 1e-4 (cosine) |
+| Batch size | 4 × 8 grad accum = effective 32 |
+| Precision | BF16 |
+
+## Related Resources
+
+| Resource | Link |
+|----------|------|
+| Pretrain Data | [zihaojing/DQFormer-pretrain-data](https://huggingface.co/datasets/zihaojing/DQFormer-pretrain-data) |
+| SFT Data | [zihaojing/DQFormer-sft-data](https://huggingface.co/datasets/zihaojing/DQFormer-sft-data) |
+| Encoder (Stage 1) | [zihaojing/DQFormer-encoder](https://huggingface.co/zihaojing/DQFormer-encoder) |
+| Code | [selmiss/DQ-Former](https://github.com/selmiss/DQ-Former) |
+
+## Citation
+
+```bibtex
+@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}
+}
+```