Instructions to use allenai/StdMoE_1b14b_1T_EmoAnnealed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="allenai/StdMoE_1b14b_1T_EmoAnnealed", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/StdMoE_1b14b_1T_EmoAnnealed", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/StdMoE_1b14b_1T_EmoAnnealed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/StdMoE_1b14b_1T_EmoAnnealed

SGLang

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "allenai/StdMoE_1b14b_1T_EmoAnnealed" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "allenai/StdMoE_1b14b_1T_EmoAnnealed" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with Docker Model Runner:
```
docker model run hf.co/allenai/StdMoE_1b14b_1T_EmoAnnealed
```

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}
}