DNAGPT2_16

DNAGPT2: Genomic Large Language Model for Compression and Analysis

DNAGPT2 is a family of autoregressive (decoder-only) transformer models trained on genomic DNA sequences.

The models follow the GPT-2 architecture and are trained from scratch on a multi-species genome dataset.

Model Details

• Model Type: Causal Language Model (Decoder-only Transformer)
• Architecture: GPT-2 Small
  • Parameters: ~86 Million
  • Layers: 12
  • Heads: 12
  • Embedding Dimensions: 768
  • Context Window: 1,024 tokens
• Vocabulary Sizes: Models are available with vocabulary sizes of 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, and 8192.

Intended Use

These models are designed for:

1. DNA Compression: Used in conjunction with Arithmetic Encoding (AE) to compress genomic sequences.
2. Sequence Modeling: Next-token prediction for DNA sequences.

Input: Raw DNA sequences containing the characters A, C, G, T. Output: Logits/Probabilities for the next token in the sequence.

Training Data

The models were pretrained on the dataset provided by the authors of DNABERT-2.

• Composition: 135 genomes covering Vertebrata, Fungi, Protozoa, Invertebrata, and Bacteria.
• Size: Approximately 32.5 billion nucleotides.
• Preprocessing: The alphabet was restricted to A, C, G, T. The letter N (unknown/ambiguous nucleotide) was omitted from the training data.

Training Procedure

The models were trained using the PyTorch framework and the nanoGPT recipe.

• Tokenizer: Byte-Pair Encoding (BPE) trained via SentencePiece.
• Epochs: 1
• Optimization: AdamW (Betas: 0.9, 0.95; Weight decay: 0.1)
• Learning Rate: Cosine decay (Max: 8e-4, Min: 8e-5) with linear warmup.
• Batch Size: $2^{19}$ tokens per step.
• Hardware: Single NVIDIA A40 GPU.

Performance

The models were evaluated on their ability to compress DNA sequences (measured in bits per symbol or bps) using Arithmetic Encoding. Lower is better.

The DNAGPT2_32 model outperforms general-purpose compressors (gzip) and competitive deep learning models like hyenaDNA and megaDNA on the evaluated datasets.

How to Use

The model is compatible with the Hugging Face transformers library.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Select the model variant (e.g., vocab size 128 or 32)
# Replace with the specific repository path if hosted on HF Hub
hf_model_repository = "vojtam/DNAGPT2_128" 

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    hf_model_repository, 
    trust_remote_code=True
).to(device)

tokenizer = AutoTokenizer.from_pretrained(
    hf_model_repository, 
    trust_remote_code=True
)

# Inference Example
dna_sequence = "ACGTTGCAAACG"
token_ids = tokenizer.encode(dna_sequence, return_tensors="pt").to(device)

with torch.no_grad():
    logits = model(token_ids).logits

print(f"Input: {dna_sequence}")
print(f"Logits shape: {logits.shape}")