README.md

---
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- biology
- genomics
- long-context
arxiv: 2502.07272
---

# GENERator-v2-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-v2, a generative genomic foundation with enhanced performance in eukaryotic domain. More technical details are coming soon...

Python scripts for downstream analysis are available on Github: [https://github.com/GenerTeam/GENERator](https://github.com/GenerTeam/GENERator).

## How to use
### Simple example1:  generation

```python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model.
tokenizer = AutoTokenizer.from_pretrained("GenerTeam/GENERator-v2-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GenerTeam/GENERator-v2-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

```python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("GENERator-v2-eukaryote-1.2b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("GENERator-v2-eukaryote-1.2b-base")

# Get model configuration
config = model.config
max_length = config.max_position_embeddings

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

# Truncate each sequence to the nearest multiple of 6
processed_sequences = [tokenizer.bos_token + seq[:len(seq)//6*6] for seq in sequences]

# Tokenization
tokenizer.padding_side = "right"
inputs = tokenizer(
    processed_sequences,
    add_special_tokens=True,
    return_tensors="pt",
    padding=True,
    truncation=True,
    max_length=max_length
)

# Model Inference
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 (EOS) embedding
last_token_indices = attention_mask.sum(dim=1) - 1
eos_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("EOS (Last Token) Embeddings:", eos_embeddings)
print("Mean Pooling Embeddings:", mean_embeddings)

# ============================================================================
# Additional notes:
# - The preprocessing step ensures sequences are multiples of 6 for 6-mer tokenizer
# - For causal LM, the last token embedding (EOS) is commonly used
# - Mean pooling considers all tokens including BOS and content tokens
# - The choice depends on your downstream task requirements
# - Both methods handle variable sequence lengths via attention mask
# ============================================================================

```

## 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}, 
}
```