DaisyChainAI/daisychain-genomics

🌼 DaisyChain — Genomics

A modular genomic mind: four dense ~74M DNA/RNA specialists (≈295M params total, under Carbon-500M) behind a learned router. Instead of one monolithic foundation model, DaisyChain trains one crisp specialist per biological domain — each distilled per-domain from Carbon-500M — and routes each sequence to its home specialist.

Specialist	Domain	Params
eukaryote	Eukaryotic genomic DNA	~74M
prokaryote	Bacterial / prokaryotic DNA	~74M
mrna	Mature mRNA (coding transcript)	~74M
mrna_splice	Pre-mRNA / splice-site regions	~74M

The router

A small learned router reads each specialist's surprise (bits/base) and a PCA of its hidden state, then predicts the home domain — recovering the bias-corrections a plain argmin-perplexity rule misses. Held-out routing accuracy: 94.8% (vs 87.5% argmin). Only one ~74M specialist runs per query, so inference is ~7× cheaper per token than the 500M monolith.

How each specialist is built

Interleaved continued pretraining (next-token CE on its domain) and offline knowledge distillation from Carbon-500M (soft-target + a factorized per-nucleotide variant via Carbon's FNS branch) — i.e. cBTM-style domain experts, iterated per expert.

Capability vs Carbon-500M (the fair baseline)

metric	DaisyChain	Carbon-500M
likelihood (bits/base, ↓)	1.86	1.75
seq-recovery eukaryote (↑)	31.8%	42.2%
seq-recovery bacteria (↑)	34.0%	49.5%

Behind a 500M/1T-token monolith but within striking distance at ~15% of the active compute — and the gap keeps closing with more per-domain training (work in progress).

Usage

from daisychain import DaisyChain
dc = DaisyChain(root=".", device="cpu")
home, bits_per_base = dc.route("ACGTACGT...")   # which domain?
print(home, bits_per_base)
print(dc.generate(home, length=180))            # sample from the home specialist

Files: daisychain.py (inference), model.py / specialist_presets.py / spike_tokenizer.py / registry.py (architecture), tokenizer.json, <domain>/model.safetensors (the 4 specialists), router2.pt (router).

Interactive demo: the DaisyChain Space routes DNA in real time.

Citation

If you use these models, please cite the author — Dean Byrne (Quazim0t0):

@misc{byrne2026daisychain,
  title        = {DaisyChain Genomics: A Modular Mixture of Per-Domain Distilled Genomic Specialists},
  author       = {Byrne, Dean},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/DaisyChainAI/daisychain-genomics}},
  note         = {DaisyChainAI (Quazim0t0). Four ~74M DNA/RNA specialists distilled per-domain
                  from Carbon-500M behind a learned router}
}

Built on

DaisyChain stands on these works:

@misc{carbon2025,
  title        = {Carbon: Genomic Foundation Models},
  author       = {{HuggingFaceBio}},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/HuggingFaceBio/Carbon-500M}}
}

@article{li2022branchtrainmerge,
  title   = {Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models},
  author  = {Li, Margaret and Gururangan, Suchin and Dettmers, Tim and Lewis, Mike and
             Althoff, Tim and Smith, Noah A. and Zettlemoyer, Luke},
  journal = {arXiv preprint arXiv:2208.03306},
  year    = {2022}
}

@article{gururangan2023cbtm,
  title   = {Scaling Expert Language Models with Unsupervised Domain Discovery},
  author  = {Gururangan, Suchin and Li, Margaret and Lewis, Mike and Shi, Weijia and
             Althoff, Tim and Smith, Noah A. and Zettlemoyer, Luke},
  journal = {arXiv preprint arXiv:2303.14177},
  year    = {2023}
}

@article{sukhbaatar2024btx,
  title   = {Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM},
  author  = {Sukhbaatar, Sainbayar and Golovneva, Olga and Sharma, Vasu and Xu, Hu and
             Lin, Xi Victoria and Roziere, Baptiste and Kahn, Jacob and Li, Daniel and
             Yih, Wen-tau and Weston, Jason and Li, Xian},
  journal = {arXiv preprint arXiv:2403.07816},
  year    = {2024}
}

@article{hinton2015distilling,
  title   = {Distilling the Knowledge in a Neural Network},
  author  = {Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
  journal = {arXiv preprint arXiv:1503.02531},
  year    = {2015}
}

@inproceedings{furlanello2018born,
  title     = {Born-Again Neural Networks},
  author    = {Furlanello, Tommaso and Lipton, Zachary C. and Tschannen, Michael and
               Itti, Laurent and Anandkumar, Anima},
  booktitle = {ICML},
  year      = {2018}
}

@inproceedings{gururangan2020dapt,
  title     = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
  author    = {Gururangan, Suchin and Marasovi{\'c}, Ana and Swayamdipta, Swabha and
               Lo, Kyle and Beltagy, Iz and Downey, Doug and Smith, Noah A.},
  booktitle = {ACL},
  year      = {2020}
}