Model reference · open weights
PlantCaduceus_l32 is an open-weight embedding model from kuleshov-group. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | kuleshov-group |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Runs with | transformers |
| Released | 2024-05-19 |
| Popularity | 707 downloads / month |
| Licence | Open weights |
About
PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the Caduceus and Mamba architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes:
We would highly recommend using the largest model (PlantCaduceus_l32) for the zero-shot score estimation.
from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer
import torch
model_path = 'kuleshov-group/PlantCaduceus_l32'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
sequence = "ATGCGTACGATCGTAG"
encoding = tokenizer.encode_plus(
sequence,
return_tensors="pt",
return_attention_mask=False,
return_token_type_ids=False
)
input_ids = encoding["input_ids"].to(device)
with torch.inference_mode():
outputs = model(input_ids=input_ids, output_hidden_states=True)
@article{Zhai2025CrossSpecies,
author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr},
title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model},
journal = {Proceedings of the National Academy of Sciences},
year = {2025},
volume = {122},
number = {24},
pages = {e2421738122},
doi = {10.1073/pnas.2421738122},
url = {https://doi.org/10.1073/pnas.2421738122}
}
Jingjing Zhai (jz963@cornell.edu)
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys plantcaduceus-l32 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (plantcaduceus-l32 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"plantcaduceus-l32","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.