StdMoE_1b14b_1T_EmoAnnealed
The ablation checkpoint released alongside EMO: Pretraining Mixture of Experts for Emergent Modularity — referred to as EMO-anneal in Appendix B.4 / Table 3 of the paper.
1B-active / 14B-total parameter Mixture-of-Experts model (128 experts: 127 routed + 1 shared, k=8 active per token) pretrained on 1T tokens of the OLMoE pretraining mix with the standard MoE objective, then annealed for an additional 50B tokens under the EMO document-level expert pool constraint. Used to test whether modularity can be induced after pretraining instead of during it.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "allenai/StdMoE_1b14b_1T_EmoAnnealed"
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
@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}
}
License
This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines