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alt.freeweights.olmo / StdMoE_1b4b_130B
From: allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject:

StdMoE_1b4b_130B

Date: 2026-11-15Commit: d14779cSigned: 2DC28CC7B26A283B ✓

Text generation model with 3.9B parameters. Safetensors weights, 15.6 GB in all. Published by allenai under Apache-2.0.

3.9B params15.6GBStdMoE_1b4b_130Bsafetensorspermissive
FactsParams3.9BSize15.6 GBFormatssafetensorsTasktext generationLicenseApache-2.0From sourcepermissive
$ fw get allenai/StdMoE_1b4b_130B
# manifest sha256 29aa42e5ad9d… · 15.6 GB · 17 files
$ fw verify ./StdMoE_1b4b_130B allenai/StdMoE_1b4b_130B
From sourceHosted (signed)allenai/StdMoE_1b4b_130BLast check: 2026-11-15 03:51 UTC
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Overview

Size
15.6 GB
Weights
15.6 GB (safetensors)
Parameters
3.9B
Formats
safetensors
Task
text generation
License
Apache-2.0 (permissive)
Files
17, each with a sha256
Publisher
allenai
Added
2026-11-15
Section
alt.freeweights.olmo
Lineage

Original release from the publisher.

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From sourcehuggingface.co2026-11-15 03:51 UTCpassed

Model card

From the publisher at commit d14779c. Shown as plain text with links; images open at the publisher.

StdMoE_1b4b_130B#

A memory-matched standard MoE baseline released alongside EMO: Pretraining Mixture of Experts for Emergent Modularity — referred to as "Reg. MoE @ 32" in Figure 1 of the paper. Not midtrained.

1B-active / 4B-total parameter Mixture-of-Experts model (32 routed experts + 1 shared, k=8 active per token) pretrained from scratch on 130B tokens of the OLMoE pretraining mix with the standard MoE objective. Provides a memory-matched comparison point against 32-expert subsets carved out of the larger 1B/14B EMO models.

Usage#

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/StdMoE_1b4b_130B"
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

Files

17 files · 15.6 GBWeights 15.6 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 17 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md1.86 KB88a61b757c091631ebf75e60213a31bb5963de59b62b21e11023ce9b5b6f2893
chat_template.jinja196 B153280e3ff55d19da1398bdb3914ee2a51b80429bfaedde11d7d216c39db80f3
config.json1.04 KB400ead65395ddd34524954d0b19726c93b0e196e6d110de876df1645f20c7cf6
configuration_emo.py11.7 KBf1bd419d8dd926cf7d15131b8192938a0feda1b003586320fa56de20360e4bf2
generation_config.json69 B49b7b376c9a77281e37b1544e00f6b83ffe1d46bd47ff1c5ee04a7d642ecbe2b
merges.txt917 KBb6fe424e334903f7fb84d3a106d9730455f4744b9fe3c21ee136d97a00e72502
model-00001-of-00004.safetensors4.99 GBbc87e4d7d515f45b6ab09fce30786d231c599d6722edac12f5a3165d9fc65213
model-00002-of-00004.safetensors4.99 GB6d95e91419bf4663487076107a9fe0f1b0e50c8e78ea2172b3d24028d1a7b41a
model-00003-of-00004.safetensors4.80 GB939f5de55c7c5986c4214bb9000d2e8604313765424768831d056580e49160d9
model-00004-of-00004.safetensors822 MBdee99b92192f0beb6b947b210f230fcc49ca8449140b75afa14064bdd8bc2421
model.safetensors.index.json147 KB80fa3f6631858a833977ad648b87e069c5c33e383972ab7cf3f53bd3c2d95b97
modeling_emo.py55.2 KBe10fb25a662d581b25708c7b1d640ba22c7547824a1f4f00c1fc39f08801d32b
special_tokens_map.json207 B4cb2b52960bababa8ec27153831e405ef7811e3fad1226106e09c28e715cfb21
tokenizer.json7.14 MB73fd5254624f39a88e3faac6a8e11300fc3c735ed37880d4f4f08db898eaecca
tokenizer_config.json4.31 KB733b2ec0f743cf206dd20335eacbd4427c85487fd6f5216bb361f92a85314315
vocab.json1.61 MB9e14712c91b37c7aab74b1306baa46ac342d620637a4b44523cdc3aec7d24195
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
d14779c95c pinned now2026-11-15 03:51 UTC17 files · 15.6 GB

Use it

$ fw get allenai/StdMoE_1b4b_130B
# manifest sha256 29aa42e5ad9d… · 15.6 GB · 17 files
$ fw verify ./StdMoE_1b4b_130B allenai/StdMoE_1b4b_130B
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/allenai/StdMoE_1b4b_130B.json --dir ./StdMoE_1b4b_130B $ python3 fw.py verify ./StdMoE_1b4b_130B ./allenai__StdMoE_1b4b_130B.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download allenai/StdMoE_1b4b_130B --revision d14779c95cd82372c56d6ad94503cc5cd0a65cce --local-dir ./StdMoE_1b4b_130B
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("allenai/StdMoE_1b4b_130B", revision="d14779c95cd82372c56d6ad94503cc5cd0a65cce") model = AutoModelForCausalLM.from_pretrained("allenai/StdMoE_1b4b_130B", revision="d14779c95cd82372c56d6ad94503cc5cd0a65cce", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve allenai/StdMoE_1b4b_130B --revision d14779c95cd82372c56d6ad94503cc5cd0a65cce
Run at home

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
88a61b757c091631ebf75e60213a31bb5963de59b62b21e11023ce9b5b6f2893
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: allenai <huggingface.co/allenai>Newsgroups: alt.freeweights.olmoSubject: StdMoE_1b4b_130BDate: 2026-11-15Commit: d14779cSigned: 2DC28CC7B26A283B ✓
Upstream
allenai/StdMoE_1b4b_130B
Pinned commit
d14779c95cd82372c56d6ad94503cc5cd0a65cce
Manifest
StdMoE_1b4b_130B.json · .minisig
Manifest sha256
29aa42e5ad9d8d2a07b4f0e206d6180f6dc9e4a330d4ffb073f39228392e228e
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 03:51 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/allenai/StdMoE_1b4b_130B.json $ curl -O https://freeweights.allternit.com/manifests/allenai/StdMoE_1b4b_130B.json.minisig $ minisign -Vm StdMoE_1b4b_130B.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

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Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download allenai/StdMoE_1b4b_130B
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Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. StdMoE_1b4b_130B.json. Provenance. Takedown history: none.
Source
huggingface.co/allenai/StdMoE_1b4b_130B

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