GENERator-eukaryote-1.2b-base model

Important Notice

If you are using GENERator for sequence generation, please ensure that the length of each input sequence is a multiple of 6. This can be achieved by either:

1. Padding the sequence on the left with 'A' (left padding);
2. Truncating the sequence from the left (left truncation).

This requirement arises because GENERator employs a 6-mer tokenizer. If the input sequence length is not a multiple of 6, the tokenizer will append an '<oov>' (out-of-vocabulary) token to the end of the token sequence. This can result in uninformative subsequent generations, such as repeated 'AAAAAA'.

We apologize for any inconvenience this may cause and recommend adhering to the above guidelines to ensure accurate and meaningful generation results.

Abouts

In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 1.2B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. Our evaluations demonstrate that the GENERator consistently achieves state-of-the-art performance across a wide spectrum of benchmarks, including Genomic Benchmarks, NT tasks, and our newly proposed Gener tasks.

Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of enhancer sequences that regulate gene expression during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.

For more technical details, please refer to our paper GENERator: A Long-Context Generative Genomic Foundation Model. The code and implementation details are available on Github: https://github.com/GenerTeam/GENERator.

How to use

Simple example1: generation

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model.
tokenizer = AutoTokenizer.from_pretrained("GenerTeam/GENERator-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-eukaryote-1.2b-base")
config = model.config

max_length = config.max_position_embeddings

# Define input sequences.
sequences = [
    "ATGAGGTGGCAAGAAATGGGCTAC",
    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
]

def left_padding(sequence, padding_char='A', multiple=6):
    remainder = len(sequence) % multiple
    if remainder != 0:
        padding_length = multiple - remainder
        return padding_char * padding_length + sequence
    return sequence

def left_truncation(sequence, multiple=6):
    remainder = len(sequence) % multiple
    if remainder != 0:
        return sequence[remainder:]
    return sequence

# Apply left_padding to all sequences
# padded_sequences = [left_padding(seq) for seq in sequences]

# Apply left_truncation to all sequences
truncated_sequences = [left_truncation(seq) for seq in sequences]

# Process the sequences
sequences = [tokenizer.bos_token + sequence for sequence in truncated_sequences]

# Tokenize the sequences
tokenizer.padding_side = "left"
inputs = tokenizer(
    sequences,
    add_special_tokens=False,
    return_tensors="pt",
    padding=True,
    truncation=True,
    max_length=max_length
)

# Generate the sequences
with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=32, temperature=0.00001, top_k=1)

# Decode the generated sequences
decoded_sequences = tokenizer.batch_decode(outputs, skip_special_tokens=True)

# Print the decoded sequences
print(decoded_sequences)

# It is expected to observe non-sense decoded sequences (e.g., 'AAAAAA')
# The input sequences are too short to provide sufficient context.

Simple example2: embedding

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model.
tokenizer = AutoTokenizer.from_pretrained("GENERator-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GENERator-eukaryote-1.2b-base")

config = model.config
max_length = config.max_position_embeddings

# Define input sequences.
sequences = [
    "ATGAGGTGGCAAGAAATGGGCTAC",
    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAAT"
]

# Tokenize the sequences with add_special_tokens=True to automatically add special tokens,
# such as the BOS EOS token, at the appropriate positions.
tokenizer.padding_side = "right"
inputs = tokenizer(
    sequences,
    add_special_tokens=True,
    return_tensors="pt",
    padding=True,
    truncation=True,
    max_length=max_length
)

# Perform a forward pass through the model to obtain the outputs, including hidden states.
with torch.inference_mode():
    outputs = model(**inputs, output_hidden_states=True)

# Retrieve the hidden states from the last layer.
hidden_states = outputs.hidden_states[-1]  # Shape: (batch_size, sequence_length, hidden_size)

# Use the attention_mask to determine the index of the last token in each sequence.
# Since add_special_tokens=True is used, the last token is typically the EOS token.
attention_mask = inputs["attention_mask"]
last_token_indices = attention_mask.sum(dim=1) - 1  # Index of the last token for each sequence

# Extract the embedding corresponding to the EOS token for each sequence.
seq_embeddings = []
for i, token_index in enumerate(last_token_indices):
    # Fetch the embedding for the last token (EOS token).
    seq_embedding = hidden_states[i, token_index, :]
    seq_embeddings.append(seq_embedding)

# Stack the embeddings into a tensor with shape (batch_size, hidden_size)
seq_embeddings = torch.stack(seq_embeddings)

print("Sequence Embeddings:", seq_embeddings)

Citation

@misc{wu2025generator,
      title={GENERator: A Long-Context Generative Genomic Foundation Model}, 
      author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang},
      year={2025},
      eprint={2502.07272},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.07272}, 
}