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EnhancAR-Sorted

aduncan94 / EnhancAR-Sorted

Run on huggingface.co
huggingface.co
Total runs: 49
24-hour runs: 0
7-day runs: -9
30-day runs: 10
Model's Last Updated: April 16 2026
text-generation

Introduction of EnhancAR-Sorted

Model Details of EnhancAR-Sorted

EnhancAR Sorted

EnhancAR Sorted is an autoregressive generative model of enhancer homology families, trained on 233,158,475 enhancers extracted from 241 vertebrate genomes. By "unrolling" homology families (enhancer sequences are sorted into sets of homology sequences, and input data is sequences concatenated to each other with a separator token delimiting different sequences), EnhancAR-Sorted learns to generate new sequences that conserve the function of prompt sequences. We demonstrate that this can be used to design new enhancers "by example", which is particularly useful when the function of enhancers is not known a priori.

Model Details
  • Developed by: Kevin K. Yang, Alex J. Lee, Andrew G. Duncan, Micaela E Consens, Lorin Crawford, Jennifer A. Mitchell, and Alan M. Moses
  • Model type: Hybrid state-space-model transformer architecture with mixture-of-experts
  • License: MIT
Model Sources
Uses

EnhancAR-sorted is a generative enhancer model that can be used for:

  • Unconditional generation of enhancer sequences
  • Conditional design of shortened enhancer sequences
Getting Started with EnhancAR-Sorted

Requirements :

  • PyTorch: 2.7.1
  • CUDA: 12.8 and above 
pip install transformers==4.48.2 huggingface_hub

Example generation :

The following shows an example for generating an unconditional sequence. To generate a sequence conditionally, replace the input_sequence with a string of the form: {homolog A/homolog B/.../homolog N}.

For a full example, please see the Google Colab 

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

config = AutoConfig.from_pretrained("aduncan94/EnhancAR-Sorted", trust_remote_code=True)
config.use_mamba_kernels = False
model = AutoModelForCausalLM.from_pretrained("aduncan94/EnhancAR-Sorted", trust_remote_code=True, config=config).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("aduncan94/EnhancAR-Sorted", trust_remote_code=True)

input_sequence = "{"
inputs = tokenizer(input_sequence, return_tensors="pt", return_token_type_ids=False, add_special_tokens=False)

outputs = generate_sequence(inputs, model, tokenizer)
output = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]

print(output)
Technical Specifications
Compute Infrastructure
  • The 170M-parameter model was trained on 8 MI300X GPUs
Citation

If you use the code, models, or results, please cite our preprint

Runs of aduncan94 EnhancAR-Sorted on huggingface.co

49
Total runs
0
24-hour runs
-2
3-day runs
-9
7-day runs
10
30-day runs

More Information About EnhancAR-Sorted huggingface.co Model

More EnhancAR-Sorted license Visit here:

https://choosealicense.com/licenses/mit

EnhancAR-Sorted huggingface.co

EnhancAR-Sorted huggingface.co is an AI model on huggingface.co that provides EnhancAR-Sorted's model effect (), which can be used instantly with this aduncan94 EnhancAR-Sorted model. huggingface.co supports a free trial of the EnhancAR-Sorted model, and also provides paid use of the EnhancAR-Sorted. Support call EnhancAR-Sorted model through api, including Node.js, Python, http.

EnhancAR-Sorted huggingface.co Url

https://huggingface.co/aduncan94/EnhancAR-Sorted

aduncan94 EnhancAR-Sorted online free

EnhancAR-Sorted huggingface.co is an online trial and call api platform, which integrates EnhancAR-Sorted's modeling effects, including api services, and provides a free online trial of EnhancAR-Sorted, you can try EnhancAR-Sorted online for free by clicking the link below.

aduncan94 EnhancAR-Sorted online free url in huggingface.co:

https://huggingface.co/aduncan94/EnhancAR-Sorted

EnhancAR-Sorted install

EnhancAR-Sorted is an open source model from GitHub that offers a free installation service, and any user can find EnhancAR-Sorted on GitHub to install. At the same time, huggingface.co provides the effect of EnhancAR-Sorted install, users can directly use EnhancAR-Sorted installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

EnhancAR-Sorted install url in huggingface.co:

https://huggingface.co/aduncan94/EnhancAR-Sorted

Url of EnhancAR-Sorted

EnhancAR-Sorted huggingface.co Url

Provider of EnhancAR-Sorted huggingface.co

aduncan94
ORGANIZATIONS
huggingface.co

Other API from aduncan94

 huggingface.co
aduncan94/EnhancAR

Total runs: 89
Run Growth: -138
Growth Rate: -155.06%
Updated:April 16 2026