Model release

nomic-embed-text-v2-moe

nomic-embed-text-v2-moe is an open-weight model release in the Nomic Embed family from Nomic AI1, version nomic-embed-text-v2-moe, released Feb 2025. Its weights can be downloaded by the public1 under the Apache License 2.01.

A multilingual text embedding model that uses a mixture-of-experts design (8 experts, top-2 routing), with 475M total and 305M active parameters. It covers about 100 languages, produces 768-dimensional embeddings that can be truncated to 256 through Matryoshka training, and accepts inputs of up to 512 tokens. Nomic released it in February 2025.1 2 3

Model hub: Model card (Hugging Face) (external site: huggingface.co)
Model hub: GGUF build (external site: huggingface.co)
Paper: Technical report (arXiv 2502.07972) (external site: arxiv.org)
Repository: contrastors training code (external site: github.com)

On AI on Hugging Face: nomic-ai/nomic-embed-text-v2-moe, nomic-ai/nomic-embed-text-v2-moe-GGUF

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Feb 2025

Availability and license

Overall availability

Public

Downloadable from Hugging Face without gating.1

Availability fact review: not separately recorded

Availability is separate from permission: read the license before using or redistributing.

Apache License 2.0 (external site: huggingface.co)1
Applies to: Weights · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0
Apache License 2.0 (contrastors) (external site: raw.githubusercontent.com)7
Applies to: Code · SPDX Apache-2.0 · on the rubric’s OSI-approved software-license list · fact-level rights review not recorded
Plain-language guide to the Apache License 2.0

Component reuse rights

A readable or downloadable component is not automatically reusable. These indicators concern recorded license evidence, not system certification.

weights: Unknown — no complete fact-level rights review
code: Unknown — no complete fact-level rights review
data: Unknown — no complete fact-level rights review
documentation: Unknown — no complete fact-level rights review

No complete system-rights review is recorded for this release.

Model-disclosure tier

Computed from the checklist below using USASI rubric v0.2. An editorial category, not a certification.

Open-weight

The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.

How tiers are computed

Public materials checklist

Items for a model under USASI rubric v0.2. Unknown means unassessed or insufficient evidence.

Public materials checklist for nomic-embed-text-v2-moe
Item	Status	Notes and evidence
WeightsCan the general public download the model parameters for this release?	Public	1
Inference codeIs code for running the model published?	Public	The card documents use with Sentence Transformers and Transformers (with trust_remote_code for the custom architecture) and recommends Nomic's megablocks fork for GPU performance.1
Training codeIs the code used to train the model published?	Public	The card and technical report point to the contrastors repository.1 2 6
Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness.	Unknown	Not assessed.
Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable.	Unknown	Not assessed.
Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient.	Unknown	Not assessed.
Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above.	Partial	The card says training used 1.6 billion multilingual pairs and that the training data is released, pointing to the contrastors repository. That README documents access (with a Nomic Atlas account) to the nomic-embed-text-v1 dataset but does not separately describe the v2 multilingual data.1 6
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The card summarizes the recipe: weakly supervised contrastive pretraining, then supervised fine-tuning, with consistency filtering and Matryoshka representation learning; the technical report gives details.1 2
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Public	The technical report's abstract says code, models, and evaluation data are open sourced; the card shows BEIR and MIRACL results.2 1

What it is useful for

Multilingual retrieval, including retrieval-augmented generation. Queries and documents must carry the task prefixes search_query and search_document.1

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.

The card recommends the 256-dimension embeddings when storage or compute is a concern and notes that the mixture-of-experts architecture may need more resources than comparable dense models.1

Organization context

Nomic AI

Provenance and derivatives

Trained by Nomic from nomic-xlm-2048, which Nomic made by swapping XLM-RoBERTa Base's learned position embeddings for rotary embeddings and training further on CC100. The released model is fine-tuned from the contrastively pretrained checkpoint nomic-embed-text-v2-moe-unsupervised.1 4 5

Derived from: nomic-embed-text-v2-moe-unsupervised (external site: huggingface.co) — Contrastive-pretraining checkpoint from Nomic.
Derived from: nomic-xlm-2048 (external site: huggingface.co) — Nomic's RoPE variant of FacebookAI/xlm-roberta-base.

Other releases in the Nomic Embed family

nomic-embed-text-v1.5Model-disclosure tier (USASI rubric v0.2): Open-weight

Nomic Embed family overview

What this catalog does not know

Unknown means the sources reviewed for this record do not document it. It is not evidence that something does not exist.

Training-data information: unknown.
Training-data access: unknown.
Complete training pipeline: unknown.

Have a primary source? How to report a correction.

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S. headquarters

Developed and published by Nomic, Inc., which states that its headquarters is in New York City. The model starts from nomic-xlm-2048, Nomic's modified XLM-RoBERTa Base.1 8 5

Assessed Sep 29, 2026

Sources

1.
nomic-ai/nomic-embed-text-v2-moe model card (external site: huggingface.co)
Nomic · Model card · accessed Sep 29, 2026
2.
Training Sparse Mixture Of Experts Text Embedding Models (arXiv 2502.07972) (external site: arxiv.org)
Nomic · Paper · published Feb 11, 2025 · accessed Sep 29, 2026
3.
Nomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model (external site: simonwillison.net)
Simon Willison · News report · published Feb 12, 2025 · accessed Sep 29, 2026
4.
nomic-ai/nomic-embed-text-v2-moe-unsupervised model card (external site: huggingface.co)
Nomic · Model card · accessed Sep 29, 2026
5.
nomic-ai/nomic-xlm-2048 model card (external site: huggingface.co)
Nomic · Model card · accessed Sep 29, 2026
6.
nomic-ai/contrastors (external site: github.com)
Nomic · Repository · accessed Sep 29, 2026
7.
contrastors LICENSE (external site: raw.githubusercontent.com)
Nomic · License · accessed Sep 29, 2026
8.
Careers | Nomic (external site: nomic.ai)
Nomic · Official page · accessed Sep 29, 2026

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Cite this page

APA style

USASI. (2026, September 29). nomic-embed-text-v2-moe. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/nomic-embed-text-v2-moe/

BibTeX

@misc{usasi_nomic_embed_text_v2_moe,
  author = {{USASI}},
  title = {nomic-embed-text-v2-moe},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/nomic-embed-text-v2-moe/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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