GENERator-eukaryote-3b-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 3B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. The extensive and diverse pre-training data endow the GENERator with enhanced understanding and generation capabilities across various organisms.

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

Example 1: Sequence Generation

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "GenerTeam/GENERator-eukaryote-3b-base",
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
    dtype=torch.bfloat16,
).cuda().eval()

tokenizer = AutoTokenizer.from_pretrained(
    "GenerTeam/GENERator-eukaryote-3b-base",
    trust_remote_code=True,
)

# Define input sequences.
sequences = [
    "ATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG",
    "ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT"
]

# Truncate each sequence to the nearest multiple of 6
processed_sequences = ["<s>" + seq[len(seq)%6:] for seq in sequences]

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

# Generate the sequences
with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=32, do_sample=False)

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

# Print the decoded sequences
print(decoded_sequences)

Example 2: Embedding Extraction

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "GenerTeam/GENERator-eukaryote-3b-base",
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
    dtype=torch.bfloat16,
).cuda().eval()

tokenizer = AutoTokenizer.from_pretrained(
    "GenerTeam/GENERator-eukaryote-3b-base",
    trust_remote_code=True,
)

# Define input sequences.
sequences = [
    "ATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCGATCG",
    "ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT"
]

# Truncate each sequence to the nearest multiple of 6
processed_sequences = ["<s>" + seq[len(seq)%6:] for seq in sequences]

# Tokenize the sequences
inputs = tokenizer(
    processed_sequences,
    add_special_tokens=False,
    return_tensors="pt",
    padding=True,
    padding_side="right",
).to("cuda")

with torch.inference_mode():
    outputs = model(**inputs, output_hidden_states=True)

hidden_states = outputs.hidden_states[-1]
attention_mask = inputs["attention_mask"]

# Option 1: Last token embedding
last_token_indices = attention_mask.sum(dim=1) - 1
last_token_embeddings = hidden_states[torch.arange(hidden_states.size(0)), last_token_indices, :]

# Option 2: Mean pooling over all tokens
expanded_mask = attention_mask.unsqueeze(-1).expand(hidden_states.size()).to(torch.float32)
sum_embeddings = torch.sum(hidden_states * expanded_mask, dim=1)
mean_embeddings = sum_embeddings / expanded_mask.sum(dim=1)

# Output
print("Last Token Embeddings:", last_token_embeddings)
print("Mean Pooling Embeddings:", mean_embeddings)

# ============================================================================
# The choice depends on your downstream task requirements
# - Last token embeddings capture more localized gene-level information (e.g., strand, codon phase).
# - Mean pooling embeddings capture species-level information.
# ============================================================================

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}, 
}