README.md

---
license: apache-2.0
task_categories:
- graph-ml
tags:
- biology
- protein
- molecule
- dna
- rna
- pretraining
---

# Cuttlefish-Encoder-Data

This repository contains the encoder pretraining dataset for **Cuttlefish**, a unified all-atom LLM designed for structure-grounded reasoning. It consists of all-atom structural graphs for molecules, proteins, DNA, and RNA in a unified parquet format, used for masked-reconstruction pretraining of the graph encoder.

- **Paper:** [Scaling-Aware Adapter for Structure-Grounded LLM Reasoning](https://huggingface.co/papers/2602.02780)
- **GitHub Repository:** [zihao-jing/Cuttlefish](https://github.com/zihao-jing/Cuttlefish)
- **Encoder Model:** [zihaojing/Cuttlefish-Encoder](https://huggingface.co/zihaojing/Cuttlefish-Encoder)

## Dataset structure

```
molecules/   # molecule encoder training data
nacid/       # DNA and RNA encoder training data
protein/     # protein encoder training data (PDB structures)
test/        # held-out test samples
```

## Schema

| Field | Description |
|---|---|
| `modality` | `"molecule"`, `"protein"`, `"dna"`, or `"rna"` |
| `node_feat` | Atom/node features (N × d) |
| `pos` | 3D coordinates in Å (N × 3) |
| `edge_index` | Spatial graph edges in COO (2 × E) |
| `edge_feat_dist` | Edge distances (E × 1, optional) |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("zihaojing/Cuttlefish-Encoder-Data")
```

## Related resources

| Resource | Link |
|---|---|
| Cuttlefish LLM | [zihaojing/Cuttlefish](https://huggingface.co/zihaojing/Cuttlefish) |
| Cuttlefish-Encoder | [zihaojing/Cuttlefish-Encoder](https://huggingface.co/zihaojing/Cuttlefish-Encoder) |
| SFT instruction data | [zihaojing/Cuttlefish-SFT-Data](https://huggingface.co/datasets/zihaojing/Cuttlefish-SFT-Data) |

## Citation

```bibtex
@article{jing2026cuttlefish,
  title     = {Cuttlefish: Scaling-Aware Adapter for Structure-Grounded LLM Reasoning},
  author    = {Jing, Zihao and Zeng, Qiuhao and Fang, Ruiyi and Li, Yan Yi and Sun, Yan and Wang, Boyu and Hu, Pingzhao},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2602.02780}
}
```