metadata

license: mit
tags:
  - biology
  - genomics
  - long-context
library_name: transformers

DNAFlash

Abouts

Dependencies

rotary_embedding_torch
einops

How to use

Simple example: embedding


import torch
from transformers import AutoTokenizer, AutoModel

# Load the tokenizer and model using the pretrained model name
tokenizer = AutoTokenizer.from_pretrained("isyslab/DNAFlash")
model = AutoModel.from_pretrained("isyslab/DNAFlash", trust_remote_code=True)


# Define input sequences
sequences = [
    "GAATTCCATGAGGCTATAGAATAATCTAAGAGAAATATATATATATTGAAAAAAAAAAAAAAAAAAAAAAAGGGG"
]

# Tokenize the sequences
inputs = tokenizer(
    sequences,
    add_special_tokens=True,
    return_tensors="pt",
    padding=True,
    truncation=True
)

# Perform a forward pass through the model to obtain the outputs, including hidden states
with torch.inference_mode():
    outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])

isyslab
/

DNAFlash

DNAFlash

Abouts

Dependencies

How to use

Simple example: embedding

Citation