This page is an index for the model checkpoints released alongside EMO: Pretraining Mixture of Experts for Emergent Modularity. The repository at allenai/EMO does not host model weights — pick the checkpoint you want from the table below.

Released models

Main release

Model	Description
`allenai/Emo_1b14b_1T`	EMO — 1B-active / 14B-total MoE pretrained on 1T tokens + 50B-token midtraining anneal. The main model from the paper.

Ablation: EMO at smaller scale

Model	Description
`allenai/Emo_1b14b_130B`	EMO trained on 130B tokens (Table 1 / Figure 11 ablation). Not midtrained.

Architecture-matched standard MoE baselines

These share architecture and data with the EMO models above; only the training objective differs (no document-level expert pool constraint).

Model	Description
`allenai/StdMoE_1b14b_1T`	Standard MoE — Reg. MoE at 1T tokens in the paper. Same setup as `Emo_1b14b_1T`.
`allenai/StdMoE_1b14b_130B`	Standard MoE — Reg. MoE at 130B tokens. Same setup as `Emo_1b14b_130B`.

Memory-matched baselines (Figure 1)

Smaller models trained from scratch at fixed memory budgets, used as comparison points for EMO expert subsets.

Model	Description
`allenai/Dense_1b_130B`	Dense @ 8 — 1B dense decoder-only Transformer trained on 130B tokens. Active-parameter-matched with 8-expert subsets of the larger EMO/StdMoE models.
`allenai/StdMoE_1b4b_130B`	Reg. MoE @ 32 — 1B-active / 4B-total standard MoE (32 routed experts) trained from scratch on 130B tokens. Memory-matched with 32-expert subsets.

EMO-anneal ablation (Appendix B.4)

Tests whether modularity can be induced after pretraining by annealing a standard MoE under the EMO objective.

Model	Description
`allenai/StdMoE_1b14b_1T_Preanneal`	Standard MoE pretrained on 1T tokens, no annealing. The starting point for the EMO-anneal experiment.
`allenai/StdMoE_1b14b_1T_EmoAnnealed`	EMO-anneal — `StdMoE_1b14b_1T_Preanneal` annealed for 50B tokens under the EMO document-level expert pool objective.

Quick start

All checkpoints require trust_remote_code=True since they use custom modeling code from the ryanyxw/transformers fork. Replace model_id with the checkpoint you want from the table above.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/Emo_1b14b_1T"  # main EMO release
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

inputs = tokenizer(["Language modeling is "], return_tensors="pt", return_token_type_ids=False)
out = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=1.0, top_p=0.7)
print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])

Citation

bibtex

@article{wang2026emo,
  title  = {EMO: Pretraining Mixture of Experts for Emergent Modularity},
  author = {Wang, Ryan and Bhagia, Akshita and Min, Sewon},
  year   = {2026},
  url    = {https://arxiv.org/abs/2605.06663}
}

allenai/EMO

EMO: Pretraining Mixture of Experts for Emergent Modularity