---
language:
- dna
library_name: transformers
tags:
- DNA
- biology
- genomics
- language-model
license: other
---

# AIDO.DNA-7B

7B-parameter DNA foundation model from the AIDO (Artificial Intelligence-Driven
Observatory) suite, trained on 10.6 billion nucleotides from 796 species. This is a
standalone HuggingFace port that loads without the
[ModelGenerator](https://github.com/genbio-ai/ModelGenerator) package.

## Architecture

| Parameter | Value |
|---|---|
| Layers | 32 |
| Attention heads | 32 |
| Embedding dimension | 4352 |
| Intermediate (MLP) size | 11584 |
| Vocabulary size | 16 |
| Positional encoding | RoPE (rotary_percent=1.0) |
| Normalization | LayerNorm |
| MLP activation | SwiGLU |
| Architecture | Pre-LN Transformer (BERT-style encoder) |
| Max sequence length | 4000 (training context; RoPE has no hard limit) |

**Vocabulary:** `[PAD]`, `[MASK]`, `[CLS]`, `[SEP]`, `[UNK]`, `A`, `G`, `C`, `T`, `U`, `N`,
`[BOS]`, `[EOS]`, `[UNUSED1]`, `[UNUSED2]`, `[UNUSED3]`

DNA sequences use single-nucleotide tokenization over `A, C, G, T, N`. Each sequence is
wrapped as `[CLS] ... [SEP]`.

> **Note on `U`:** the vocabulary is the shared AIDO RNABert vocabulary, so a `U` token
> exists (id 9) and the tokenizer will accept it. However, AIDO.DNA was pretrained on DNA
> (`A, C, G, T, N`) and **never saw `U`** during training - its embedding row is
> effectively untrained (embedding norm ~1.77, in line with the unused special tokens,
> versus ~0.69-0.97 for the trained nucleotides `A/G/C/T`). Do not feed `U` to this model;
> use `T` for thymine. The token is retained only to keep `vocab_size=16` consistent with
> the original weights.

## Pretraining

- **Objective:** Masked language modeling (MLM) on genomic DNA
- **Data:** 10.6B nucleotides from 796 genomes (Nucleotide Transformer dataset),
  single-nucleotide tokenization, 4000-nucleotide context
- **Source checkpoint:** `genbio-ai/AIDO.DNA-7B`

### Checkpoint selection

The 7B model is the largest released AIDO.DNA checkpoint and the most accurate for
functional genomics, genome mining, and unsupervised annotation. For lighter-weight
deployment use [Taykhoom/AIDO.DNA-300M](https://huggingface.co/Taykhoom/AIDO.DNA-300M).

## Parity Verification

Hidden-state representations compared against the original `genbio-ai/AIDO.DNA-7B`
weights (loaded into the genbio `RNABertForMaskedLM` reference) at all 33 representation
levels (embedding + 32 transformer layers). The embedding layer matches exactly, and the
final post-LayerNorm hidden state and MLM logits match within tight tolerance.
Intermediate layer differences are floating-point accumulation noise in the un-normalized
residual stream (relative error < 1e-6), normalized away by the final layer norm.
Attention weights over valid positions sum to 1, and padded keys receive zero probability.
Verified on PyTorch 2.7 / CUDA 12.

## Related Models

See the full [AIDO.DNA collection](https://huggingface.co/collections/Taykhoom/aidodna-6a2887b44b37853f838d2ae2).

| Model | Parameters | Notes |
|---|---|---|
| [Taykhoom/AIDO.DNA-300M](https://huggingface.co/Taykhoom/AIDO.DNA-300M) | 300M | Smaller DNA variant |
| [Taykhoom/AIDO.DNA-7B](https://huggingface.co/Taykhoom/AIDO.DNA-7B) | 7B | This model |

## Usage

### Embedding generation

```python
import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/AIDO.DNA-7B", trust_remote_code=True)
model = AutoModel.from_pretrained("Taykhoom/AIDO.DNA-7B", trust_remote_code=True)
model.eval()

sequences = ["ACGTACGTACGTACGT", "TTGCAACGTAGCTAGC"]
enc = tokenizer(sequences, return_tensors="pt", padding=True)

with torch.no_grad():
    out = model(**enc)

cls_emb   = out.last_hidden_state[:, 0, :]   # (batch, 4352) -- CLS token
token_emb = out.last_hidden_state             # (batch, seq_len, 4352)

# Intermediate layers
out_all = model(**enc, output_hidden_states=True)
layer3_emb = out_all.hidden_states[3]
```

### MLM logits

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("Taykhoom/AIDO.DNA-7B", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Taykhoom/AIDO.DNA-7B", trust_remote_code=True)
model.eval()

enc = tokenizer(["ACGT[MASK]CGTA"], return_tensors="pt")
with torch.no_grad():
    logits = model(**enc).logits   # (1, seq_len, 16)
```

### Fine-tuning

Standard HF conventions. Use `cls_emb = out.last_hidden_state[:, 0, :]` (CLS token) as
input to a task-specific head for sequence-level tasks.

## Implementation Notes

The original `genbio-ai/AIDO.DNA-7B` checkpoint requires the
[ModelGenerator](https://github.com/genbio-ai/ModelGenerator) package to load.
This port is a clean standalone re-implementation:

- All model logic is contained in `modeling_aidodna.py` and `configuration_aidodna.py`.
- `attn_implementation="sdpa"` and `attn_implementation="flash_attention_2"` are added
  (not present in the original genbio-ai implementation).
- Architecture: pre-LN Transformer with SwiGLU MLP and RoPE positional embeddings,
  identical to the AIDO.RNA family (`RNABertForMaskedLM`).
- Weights are stored as 6 sharded `model-0000X-of-00006.safetensors` files.

## Citation

```bibtex
@inproceedings{ellington2024_aidodna,
  title   = {Accurate and General {DNA} Representations Emerge from Genome Foundation Models at Scale},
  author  = {Ellington, Caleb N. and Sun, Ning and Ho, Nicholas and Tao, Tianhua and Mahbub, Sazan and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.},
  booktitle = {NeurIPS 2024 Workshop on AI for New Drug Modalities},
  year    = {2024},
  doi     = {10.1101/2024.12.01.625444}
}
```

## Credits

Original model and code by Ellington et al. Source: [GitHub](https://github.com/genbio-ai/ModelGenerator).
The HF conversion code was authored primarily by [Claude](https://claude.ai/code)
and reviewed manually by Taykhoom Dalal.

## License

GenBio AI Community License, following the original repository. See `LICENSE` for details.