HyenaDNA

Important links:

• arxiv
• blog
• colab
• huggingface
• discord

Intro

Welcome to the HyenaDNA repo! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to *1 million tokens* at *single nucleotide resolution*.

The repo is a work in progress, but we're very excited to get this in the hands of researchers, so bare with us :)

This repo is best suited for those who want to pretrain a HyenaDNA model, or try one of the downstream tasks from the paper.

For the easiest entry point though, check out the HyenaDNA colab, a self contained notebook that is Huggingface integrated. You'll be able to load pretrained weights and fine-tune on the GenomicBenchmarks dataset. Also, you'll be able to do inference and get embeddings on DNA sequences up to 450k nucleotides on the free tier. For 1 million long DNA sequences, you can get an A100 on Colab (paid tier), or run the notebook on your own machine.

Credit: much of the code is forked and extended from S4 and Safari.

Discord

Trying Discord out! Maybe it'll be conducive to sharing ideas / tips on how HyenaDNA could be applied in different ways. Feel free to post questions there.

Hugging Face pretrained weights

Check these out :) There are different model sizes, and different training sequence lengths that they can handle up to. All pretrained on a single human reference genome (hg38).

• tiny-1k
• tiny-1k-d256
• small-32k
• medium-160k
• medium-450k
• large-1m

See the suggested GPU requirements for each model.

There's a few way to use these HuggingFace weights, all with different flavors:

1. colab
2. Pytorch Lighting in this repo
3. standalone

Dependencies

For this repo, let's start with the dependancies that are needed. (If you're familiar with Docker, you can skip this section and jump to the docker setup below). The repo is built using Pytorch Lightning (a training library) and Hydra a config oriented ML library. (It'll be super helpful to get familiar with those tools.)

• clone repo, cd into it

git clone --recurse-submodules https://github.com/HazyResearch/hyena-dna.git && cd hyena-dna

• create a conda environment, with Python 3.8+

conda create -n hyena-dna python=3.8

• The repo is developed with Pytorch 1.13, using cuda 11.7

conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.7 -c pytorch -c nvidia

• install requirements:

pip install -r requirements.txt

• install Flash Attention, these notes will be helpful.

cd hyena-dna
git submodule update --init
cd flash-attention
git submodule update --init
pip install -e . --no-build-isolation

• optional fused layers for speed (takes a bit of time)

# from inside flash-attn/
cd csrc/layer_norm && pip install . --no-build-isolation

Dockerfile

Even better, if you're familar with Docker, we have an image you can pull with all the dependencies installed. It's the simplest, surest, but does require some familiarity with using Docker containers.

Slight complication - you also need to clone the flash-attn repo that's used as a submodule in the main hyena-dna repo. That means you need the --recurse-submodules flag, in case you cloned without it.

# clones main and submodule repos
git clone --recurse-submodules https://github.com/HazyResearch/hyena-dna.git && cd hyena-dna

Prepare docker container

# build the image within the hyena-dna repo (it will grab the Dockerfile here).  You need to place $USER_NAME with your own Dockerhub username.
docker build . -t $USER_NAME/hyena-dna-public

Or,

# pull already built image (our $USER_NAME is hyenadna)
docker pull hyenadna/hyena-dna-public:latest

# run the container: this will give you an interactive shell with the dependencies
docker run --gpus all -it -p80:3000 hyenadna/hyena-dna-public /bin/bash

Quick Entry point

A quick start for this the repo is to train from scratch on a small genomics dataset. Let's try this just to see if things are set up ok.

The command below should auto-download a small dataset into data/. It uses a small 2 layer HyenaDNA model with a linear decoder (head) on a binary classification task. It already beats the SotA by 7 pts (one task from GenomicBenchmarks), but we can do even better with a pretrained model.

python -m train wandb=null experiment=hg38/genomic_benchmark_scratch

Let's describe this.

• -m lets you run the script as a module (no .py used in name).
• train is calling the main train.py script that launches all training / finetuning experiments.
• wandb=null, this connects to wandb too, but for quick testing I set to null. Otherwise you can use something like wandb.group=custom_name_here.
• experiment is passing the config for experiment, using the genomic_benchmark_scratch.yaml file, located in configs/experiments/hg38/.
• You can pass other configs in the command line the same way, eg, dataset=your_custom_datset_name. But more on that later.

Loading pretrained weights

There are 2 ways to use the pretrained weights from HuggingFace:

1. HuggingFace integration (best example), via colab
2. Pytorch Lightning in this repo:

• You can clone the HuggingFace repo, and pass the ckpt path to Pytorch Lighting (the .ckpt is from Lightning actually)
• the flag is train.pretrained_model_path=/path/to/ckpt
• you'll need to make sure the model config settings are the same when launching. The config is also in the HuggingFace repo.

Standalone code (HuggingFace too)

We actually have a 3rd way, but it's really just a copy of the colab but put into this repo as a .py file (in case that's more your thing). It's HuggingFace integrated, not Pytorch Lightning, so you don't get all the bells and whistles, but it is standalone, meaning it's easier to port to your own codebase. It assumes you have all the dependencies installed already.

• see the huggingface.py script for example of inference, loading pretrained weights from HF
• and the standalone_hyenadna.py, which has all the classes you need to create a HyenaDNA model

Experiments

We share our training and dataloading code for pretraining on the human reference genome (HG38), fine-tuning on a number of downstreams, and examples of our in-context learning variants using soft prompt tokens and instruction fine-tuning. You'll need to download and preprocess on your own for now, we'll share our steps for those later.

In general, get comfortable with the configs in configs/experiments/hg38, all our (sample) experiment settings are there.

Pretraining on Human Reference Genome

First step is download the Human Reference Genome data. It's comprised of 2 files, 1 with all the sequences (the .fasta file), and with the intervals we use (.bed file).

However, you'll need to have a GCP account to download the exact files we used (from the Enformer), and it cost a little to download. At some point we'll try to upload somewhere to share that data.

The file structure should look like

data
|-- hg38/
    |-- hg38.ml.fa
    |-- hg38.ml.fa.fai
    |-- human-sequences.bed

• Download fasta (.fa format) file (of the entire human genome) into hyena-dna/data/hg38. ~24 chromosomes in the whole genome (merged into 1 file), each chromosome is a continuous sequence, basically

gsutil -u hai-gcp-hippo cp gs://basenji_barnyard/hg38.ml.fa.gz ./ && gunzip hg38.ml.fa.gz

• download the .bed file with sequence intervals (contains chromosome name, start, end, split, which then allow you to retrieve from the fasta file)

gsutil -u hai-gcp-hippo cp gs://basenji_barnyard/data/human/sequences.bed ./human-sequences.bed

launch pretraining run

python -m train wandb=null experiment=hg38/hg38_hyena model.d_model=128 model.n_layer=2 dataset.batch_size=256 train.global_batch_size=256 dataset.max_length=1024 optimizer.lr=6e-4 trainer.devices=1

Let's describe a little about this command.

• experiment=hg38/hg38_hyena passes the config for this experiment using a Hyena(DNA) model
• model.d_model=128, and model.n_layer=2 select the model width and depth, key hyparams
• dataset.max_length=1024 sets the max sequence length sampled from the dataset, the model layer max length is set from this too, or...
• model.layer.l_max # you can set the max model length manually
• model.d_inner # likewise, the reverse bottleneck with can be set manually too (default is 4x d_model)

Lots of other commands you can pass and customize, feel free to check out the experiment=hg38/hg38_hyena for details.

Note: if you plan on pretraining on your own data, make sure to preprocess your data correctly, and your samples are what you expect in the dataloader. Things like, uppercase/lowercase, unknown characters, etc. Also, if your sequences are variable length (in our setting we used fixed lengths mostly, since next token prediction should theoretically be introduced to variable length sequences) then the padding may become significant or an issue. ie, if your length range is 100-32k, then the 100 sequence will have a lot of padding, so you'll need to ignore those tokens in the loss to avoid instability in training. The padding token should be 4 by default, so you can pass this in the command line, +task.loss.ignore_index=4, or modify the config too (under task.loss).

GenomicBenchmarks

The GenomicBenchmarks is an easy to use set of datasets for sequence level classification. We use as a good entry point to try new things out.

Sample run:

python -m train wandb=null experiment=hg38/genomic_benchmark dataset_name=human_enhancers_cohn train.pretrained_model_path=/path/to/ckpt dataset.max_length=500 model.layer.l_max=1024

This runs a HyenaDNA model on one of the datasets, auto-downloaded into data/. Here are the other datasets and their stats, which you can pass into this config too. The config in configs/dataset/genomic_benchmark is setup to pull in the correct dataset metadata (num_samples, classes, etc).

Just like the quick entry point explained above, you'll need to set the flags for dataset.max_length you want to use, as well as the model.layer.l_max, which tells the model the max length you want to use. The inputs will be padded up to model.layer.l_max. eg, data sample = 500, and l_max = 1024, then it will pad 501 to l_max.

The new flag here for this fine-tune experiment is to pass a pretrained ckpt via train.pretrained_model_path=/path/to/ckpt.

There are 8 datasets in this suite, choose 1 at a time (passing the dataset.dataset_name sets the num_classes and num_seqs automatically).

# name                                num_seqs        num_classes     median len    std
# dummy_mouse_enhancers_ensembl       1210            2               2381          984.4  
# demo_coding_vs_intergenomic_seqs    100_000         2               200           0
# demo_human_or_worm                  100_000         2               200           0
# human_enhancers_cohn                27791           2               500           0
# human_enhancers_ensembl             154842          2               269           122.6
# human_ensembl_regulatory            289061          3               401           184.3
# human_nontata_promoters             36131           2               251           0
# human_ocr_ensembl                   174756          2               315           108.1

Nucleotide Transformer datasets

You'll need to see the Nucleotide Transformer paper appendix for how to download and process the datasets. We'll try and upload version + preprocessing steps later (sorry).

If you'd like to use the pretrained weights we used to finetune on, you'll need the tiny-1k-d256 weights on Huggingface.

sample run

# trains from scratch
python -m train wandb=null experiment=hg38/nucleotide_transformer dataset_name=enhancer dataset.max_length=500 model.layer.l_max=1026

Similarly with GenomicBenchmarks, we need to select which dataset to use from the 17 Nucleotide Transformer datasets.

See the dataset config in configs/dataset/nucleotide_transformer for more dataset metadata, but here's some:

Fields
name max_len n_classes n_samples metric

# enhancer 200   2  14968 MCC
# enhancer_types 200   3  14968 MCC
# H3 500   2  13468 MCC
# H3K4me1  500   2  28509 MCC
# H3K4me2  500   2  27614 MCC
# H3K4me3  500   2  33119 MCC
# H3K9ac   500   2  25003 MCC
# H3K14ac  500   2  29743 MCC
# H3K36me3 500   2  31392 MCC
# H3K79me3 500   2  25953 MCC
# H4 500   2  13140 MCC
# H4ac  500   2  30685 MCC
# promoter_all   300   2  53276 F1
# promoter_non_tata 300   2  47759 F1
# promoter_tata  300   2  5517  F1
# splice_sites_acceptor   600   2  19961 F1
# splice_sites_donor   600   2  19775 F1

The file structure for the data should look like:

data
|-- nucleotide_transformer/
    |-- enhancer/
        |-- all_test_enhancer.fasta
        |-- all_train_enhancer.fasta
    |-- H3/
        |-- H3_test.fasta
        |-- H3_train.fasta
    |-- promoter_tata/
        |-- promoter_tata_test.fasta
        |-- promoter_tata_train.fasta
    |-- ...

In-context Learning

We use the GenomicBenchmarks for exploring in-context learning (ICL). It should autodownload the data into data/.

Soft prompting example run:

python -m evals/soft_prompting_genomics

instruction fine-tune example:

python -m evals/instruction_tuned_genomics

Chromatin Profile

You'll need to see the DeepSea and repo for info how to download and preprocess.

example chromatin profile run:

python -m train wandb=null experiment=hg38/chromatin_profile dataset.ref_genome_path=/path/to/fasta/hg38.ml.fa dataset.data_path=/path/to/chromatin_profile dataset.ref_genome_version=hg38

• dataset.ref_genome_path # path to a human ref genome file (the input sequences)
• dataset.ref_genome_version # the version of the ref genome (hg38 or hg19, we use hg38)
• dataset.data_path # path to the labels of the dataset

Species Classification

You'll need to download fasta files for each species that you want to use (just the .zips, the dataloader wil unzip automatically). You can download them using the following commands:

# Human
wget -P human/ -r -nH --cut-dirs=12 --no-parent ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/009/914/755/GCA_009914755.4_T2T-CHM13v2.0/GCA_009914755.4_T2T-CHM13v2.0_assembly_structure/Primary_Assembly/assembled_chromosomes/FASTA/
# Lemur
wget -P lemur/ -r -nH --cut-dirs=11 --no-parent ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/vertebrate_mammalian/Lemur_catta/latest_assembly_versions/GCA_020740605.1_mLemCat1.pri/GCA_020740605.1_mLemCat1.pri_assembly_structure/Primary_Assembly/assembled_chromosomes/FASTA/
# House mouse
wget -P mouse/ -r -nH --cut-dirs=11 --no-parent ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/vertebrate_mammalian/Mus_musculus/latest_assembly_versions/GCA_921998355.2_A_J_v3/GCA_921998355.2_A_J_v3_assembly_structure/Primary_Assembly/assembled_chromosomes/FASTA/
# Pig
wget -P pig/ -r -nH --cut-dirs=11 --no-parent ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/vertebrate_mammalian/Sus_scrofa/latest_assembly_versions/GCA_002844635.1_USMARCv1.0/GCA_002844635.1_USMARCv1.0_assembly_structure/Primary_Assembly/assembled_chromosomes/FASTA/
# Hippo
wget -P hippo/ -r -nH --cut-dirs=11 --no-parent ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/vertebrate_mammalian/Hippopotamus_amphibius/latest_assembly_versions/GCA_023065835.1_ASM2306583v1/GCA_023065835.1_ASM2306583v1_assembly_structure/Primary_Assembly/assembled_chromosomes/FASTA/

Your folder struture should look like this:

data
|-- species/
    |-- chimpanzee/
        |-- chr1.fna
        |-- chr2.fna
        |-- ...
    |-- hippo/
        |-- chr1.fna
        |-- chr2.fna
        |-- ...
    |-- human/
        |-- chr1.fna
        |-- chr2.fna
        |-- ...
    |-- mouse/
        |-- chr1.fna
        |-- chr2.fna
        |-- ...
    |-- orangutan/
        |-- chr1.fna
        |-- chr2.fna
        |-- ...
    |-- other species ...
|-- ...

Sample species run:

python -m train wandb=null experiment=hg38/species dataset.species=[human,mouse,hippo,pig,lemur] train.global_batch_size=256 optimizer.lr=6e-5 trainer.devices=1 dataset.batch_size=1 dataset.max_length=1024 dataset.species_dir=/path/to/data/species/ model.layer.l_max=1026 model.d_model=128 model.n_layer=2 trainer.max_epochs=150 decoder.mode=last train.pretrained_model_path=null train.pretrained_model_state_hook=null

Let's break some of these args down:

• experiment=hg38/species # main config for this experiment
• dataset.species # list of species you want (and already downloaded their .fasta files)
• decoder.mode=last # using the last token to classify (instead of default pooling)
• train.pretrained_model_path # if using a pretrained model, point to it, if not, set to null
• train.pretrained_model_state_hook=null # if using a pretrained model, this will load the weights properly (and not head). if not, set to null

More advanced stuff below

Setting up downstream experiments (fine tuning)

Let's see what's needed to set up a downstream task.

The main ingredients are:

1. Model weights and model config (which are provided via HuggingFace at the top)
2. Custom dataset class and dataloader class
3. Configs for experiment, dataset, pipeline, model. Don't worry, we have examples for each of these.

Again, example run, breakdown in launch command:

python -m train wandb=null experiment=hg38/genomic_benchmark

Model config:

We talked about some of the model config setting above. We placed the model config within the experiment config for convenience (which can override, basically), but you can place in the configs/model dir if you want. There is a separate layer config at configs/model/layer. This is where it's useful to understand the Hydra config stuff.

Flags for using ultralong context (gradient checkpointing)

We have a checkpoint flag that allows ~3x less memory on a GPU (to enable longer sequences). However, this means that you may have trouble loading checkpoints if you don't set the flags correctly (they need to be True if it was pretrained with these, and False if not).

• model.checkpoint_mixer: True # set true for memory reduction
• model.checkpoint_mlp: True # set true for memory reduction

Note, if it's not in the config and you want to pass it in the commandline, you would add a + in front, like this: +model.checkpoint_mixer=True

If you get an error (like below) with the state_dict keys not matching, it's likely due to these flags, so toggle these on/off

Missing key in pretrained model! backbone.layers.0.mixer.layer.filter_fn.bias

Setting up a Dataset class

Here's a sample dataset class for a DNA downstream task.

src/dataloaders/datasets/genomic_bench_dataset.py

It's basically a standard Pytorch dataset. Place data in the data/, with something like /data/your_custom_dataset_name, so the repo can find it.

Here's a sample dataloader for a DNA downstream task. There's some more actually connecting with the HyenaDNA repo required here.

src/dataloaders/genomic_bench_dataloader.py

Notice the name is placed with _name_ = "genomic_benchmark" as a class attribute. This name is how we find it. Also, we need to add the dataloader file to the __init__, see the top of this script, src/dataloaders/__init__.py.

I would emulate this dataloader file. It's basically a way for Pytorch lightning to handle a lot of the dataloading stuff in the background. Pass params to the init that you need to create it. Notice the def setup(), this is where the dataset class is instantiated. setup() gets called in the training script (more on that later).

There are 3 dataloader functions that create the train/val/test dataloaders. (In this example, the dataset only uses train and test dataloader.)

Creating Configs

As mentioned above, the main config is the experiment config, and for our example, located here configs/experiment/hg38/genomic_benchmark.yaml.

You can think of each of these sections as their own configs too. eg, model, task, optimizer etc. You can write them in here, or have it referenced at the top (as default or overide, subtle differences).

For a new dataset, we need a new dataset config and a pipeline config. These configs get passed when they're instantiated.

The pipeline config hasn't been mentioned yet, but it's where we define a few different things. Take a look inside:

configs/pipeline/genomic_benchmark.yaml

Try to emulate this config too, which will get reference at the top of the experiment config. We select the optimizer, scheduler, name of the dataset, the task (typically classification for these downsteams, but we have other options for the decoder). Don't worry about the encoder. We do use a decoder, which is just a single MLP that maps the backbone to the number of classes we're trying to predict. When you create the dataset class, it will require a d_output for the number of classes, and the decoder will automatically pull this attribute in the background, as well as the dimension of the backbone from d_model. The decoder can also have options, like pool, where we average the token embeddings, or last or first, meaning which token we use for the MLP to learn from.

If want to train at different sequence lengths, there's a few places we would need to change too. Namely, the dataset config and the model configs. You could change these in the experiment config, or individually setup defaults in the standalone dataset / dataloader configs too, up to you.

dataset config expects a max_length to be set.

model.layer.l_max expects a length too. Usually set to the dataset max_length + 2

Launch a finetuning experiment

# example downstream task
python -m train wandb=null experiment=hg38/genomic_benchmark train.pretrained_model_path=<path_to_ckpt>

The dataset will automatically download to the data/ dir (probably), and it's not that large, ~5-10 min setup. All you need to do is download the weights from HuggingFace above, and change the configs to match the model settings, and the dataset seq_len you want to use. Might take some fumbling around to get right, but it'll be worth it!

To describe this experiment config a little more, let's dive in. It finetunes a HyenaDNA (GPT-like). Let's focus on the train arguments.

• remove_test_loader_in_eval: true # no test set in this benchmark
  We have the option to remove an extra test_loader, eg, if val and test are the same.

• pretrained_model_strict_load: False # false allows encoder/decoder to be used if new model uses it
  Set false to play nicely when loading pretrained weights

for loading backbone and not head, requires both of these flags below

• pretrained_model_path: /home/workspace/eric/safari-internal/outputs/2023-03-23/07-10-41-239444/checkpoints/val/loss.ckpt This is where we pass the pretrained model to use as a backbone

• pretrained_model_state_hook

• _name_: load_backbone This is a custom hook function that will load the backbone properly with a new MLP decoder head for the downstream task.

• freeze_backbone: false # seems to work much better if false (ie finetune entire model)
  We have the option to freeze here.

Loading a finetuned model

Next we'll show an example of loading weights (that were finetuned) on a downstream task (it will continue to train though).

• see weights from HuggingFace above.
• They are for a 2 layer, d_model=128 (width), with a max_length=1024 (sequence len)
• Place these somewhere in the repo, typically we place them in the outputs/dir.

The main things we need to do now are to update appropriate args in a config.

# path to config finetuned model config
safari-internal/configs/experiment/hg38/genomic_benchmark_load_finetuned_model.yaml

For this config, select the dataset you want to train with dataset.dataset_name, which we'll use human_nontata_promoters, since this is what the weights above are fine tuned on.

Next, you need to update train.pretrained_model_path: path_to_ckpt, to wherever you placed them in the repo.

Now we can launch a run with this:

python -m train wandb=null experiment=hg38/genomic_benchmark_load_finetuned_model

This will run the main src/train.py script.

Let's point out a few keys locations in the train.py script, since it's a little confusing where all the stuff gets called.

• loading weights occurs with the train.py, def load_state_dict() function. It actually calls a custom state hook to load gracefully (in the src/models/sequence/long_conv_lm.py, inthe load_backbone() function.

• forward prop is done in the def forward() function, inside SequenceLightning module of train.py, but realy, it calls self.task.forward(), which actually makes the call to the model. That is to say, you need to go src/tasks/tasks.py, and fine class LMTask, and its def forward() function. Here you'll see the actual call to the model. Note, the decoder head (a single MLP for classification) is separate from the main model backbone (feature extractor).

Sequence Length Warmup Callback

We have sequence length warmup scheduler, implemented using a callback, which will increase sequence length in stages during training. Basically the script will check what epoch and "stage" the training is at, and update the dataset/dataloaders to the parameters for that stage. Currently, you need to specify the stages manually in a config, the example config is at, and the relevant portion is at the bottom of the config, and here below too:

configs/experiment/hg38/hg38_hyena_seqlen_warmup_reload.yaml

Guidance: You have to be careful to know ahead of time that the batch size and seq len will fit into memory for EACH stage.

To make your dataloader compatible with the seqlen warmup, you need to implement an interface, which is init_datasets(). Here's what it looks like:

The sharp edges:

To use this callback, we'll use the sample config above, configs/experiment/hg38/hg38_hyena_seqlen_warmup_reload.yaml.

You'll need to design the stages manually, ie, what epoch and seq len you want to gradually increase the seq len (and lower batch size). Note, the epochs at each stage means how long we run that stage for (it's not cummulative).

callbacks:
  seqlen_warmup_reload:
    # epochs refers to how long to run at that stage (not cummulative!)
    # this is just a sample
    stage_params:
      - epochs: 2  # means run this stage for 2 epochs (0, and 1)
        seq_len: 1024
        batch_size: 256  # in the background, grad accum = 1, since train.global_batch_size=256
      - epochs: 2  # run for 2 epochs (2 and 3)
        seq_len: 2048
        batch_size: 128
      - epochs: 2  # run for epochs 4, 5
        seq_len: 4096  #
        batch_size: 64
      - epochs: 2  # epoch 6, 7
        seq_len: 8192  
        batch_size: 32
      - epochs: 4  #  epoch 8, 9, 10, 11
        seq_len: 16_384  # 
        batch_size: 16
      - epochs: 4  # epoch 12, 13, 14, 15
        seq_len: 32_768
        batch_size: 8

As for the other parameters you run in the command line that are important:

In the sample config, see the

• train.global_batch_size don't forget to set this! It will control the accumulate_grad_batches to keep the lr consistent each stage. eg, 256 or 128 typically (maybe 64 for very long seqs)
• dataset.batch_size now refers to the test (or final seq len and batch). the test set will always be the same
• dataset.max_length now refers to the test (or final seq len and max_length). the test set will always be the same
• model.layer.l_max needs to be set to the highest seq len +2 (the test set size)

Things to note:

Train dataset will change during training, but the test set will always be fixed. The test len/batch size is set the normal way in your command launch, ie, dataset.batch_size, dataset.

Citation

Feel free to cite us if you find our work useful :)

@article{nguyen2023hyenadna,
      title={HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution}, 
      author={Eric Nguyen and Michael Poli and Marjan Faizi and Armin Thomas and Callum Birch-Sykes and Michael Wornow and Aman Patel and Clayton Rabideau and Stefano Massaroli and Yoshua Bengio and Stefano Ermon and Stephen A. Baccus and Chris Ré},
      year={2023},
      eprint={2306.15794},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}