Model release

nomic-embed-text-v1.5

nomic-embed-text-v1.5 is an open-weight model release in the Nomic Embed family from Nomic AI1 2, version nomic-embed-text-v1.5, released Feb 14, 2024. Its weights can be downloaded by the public1 under the Apache License 2.01.

A text embedding model that updates nomic-embed-text-v1 with Matryoshka representation learning, so its 768-dimension embeddings can be shortened to as few as 64 dimensions, or binarized, with a small loss in quality. Released in February 2024, it follows v1's long-context design, which Nomic describes as supporting 8,192 tokens.2 1 3

Model hub: Model card (Hugging Face) (external site: huggingface.co)
Model hub: GGUF build (external site: huggingface.co)
Release notes: v1.5 announcement (external site: nomic.ai)
Paper: Nomic Embed technical report (arXiv 2402.01613) (external site: arxiv.org)
Repository: contrastors training code (external site: github.com)

On AI on Hugging Face: nomic-ai/nomic-embed-text-v1.5, nomic-ai/nomic-embed-text-v1.5-GGUF

Last reviewedSep 29, 2026Entry updated Sep 29, 2026Documented release Feb 14, 2024

Availability and license

Overall availability

Public

Downloadable from Hugging Face without gating. Nomic also serves the model through its hosted embedding API.1 4

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)6
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-v1.5
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, Transformers, and Transformers.js, and says trust_remote_code is no longer needed from Transformers 5.5.0 and Sentence Transformers 5.3.0.1
Training codeIs the code used to train the model published?	Public	Training code is in the contrastors repository.2 5
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.	Public	The card says the training data is released in full. The contrastors README explains how to download it from Nomic's storage after creating a Nomic Atlas account.1 2 5
Training recipeAre the training configuration and procedure documented in enough detail to follow?	Partial	The card describes the stages: start from nomic-bert-2048, contrastive training on weakly related text pairs (for example forum question-answer pairs and review title-body pairs), then fine-tuning on labeled data with hard-example mining; the technical report gives details.1
Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only.	Unknown	Not assessed.

What it is useful for

Embedding documents and queries for retrieval-augmented generation and search, and texts for clustering and classification, selected with the task prefixes search_document, search_query, clustering, and classification. Nomic's vision model nomic-embed-vision-v1.5 is aligned to its embedding space, so text and image embeddings can be compared.1

Run and use notes

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

The card shows applying layer normalization before truncating to a smaller Matryoshka dimension and then normalizing, and explains how to extend the sequence length past 2,048 tokens with dynamic RoPE scaling.1

Organization context

Nomic AI

Provenance and derivatives

Trained by Nomic from nomic-bert-2048, a long-context BERT variant Nomic developed for Nomic Embed, as an update to nomic-embed-text-v1.1 3

Derived from: nomic-bert-2048 (external site: huggingface.co) — Nomic's long-context BERT starting model.
Derived from: nomic-embed-text-v1 (external site: huggingface.co) — Previous release; v1.5 adds Matryoshka representation learning.

Other releases in the Nomic Embed family

nomic-embed-text-v2-moeModel-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.
Evaluation materials: 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-bert-2048, a BERT variant Nomic trained itself.1 7 3

Assessed Sep 29, 2026

Sources

1.
nomic-ai/nomic-embed-text-v1.5 model card (external site: huggingface.co)
Nomic · Model card · accessed Sep 29, 2026
2.
Unboxing Nomic Embed v1.5: Resizable Production Embeddings with Matryoshka Representation Learning (external site: nomic.ai)
Nomic · Announcement · published Feb 14, 2024 · accessed Sep 29, 2026
3.
Introducing Nomic Embed: A Truly Open Embedding Model (external site: nomic.ai)
Nomic · Announcement · published Feb 1, 2024 · accessed Sep 29, 2026
4.
Text Embedding | Nomic Atlas documentation (external site: docs.nomic.ai)
Nomic · Documentation · accessed Sep 29, 2026
5.
nomic-ai/contrastors (external site: github.com)
Nomic · Repository · accessed Sep 29, 2026
6.
contrastors LICENSE (external site: raw.githubusercontent.com)
Nomic · License · accessed Sep 29, 2026
7.
Careers | Nomic (external site: nomic.ai)
Nomic · Official page · accessed Sep 29, 2026

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This listing is not an endorsement, a safety assessment, or a federal approval.

Cite this page

APA style

USASI. (2026, September 29). nomic-embed-text-v1.5. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/nomic-embed-text-v1-5/

BibTeX

@misc{usasi_nomic_embed_text_v1_5,
  author = {{USASI}},
  title = {nomic-embed-text-v1.5},
  year = {2026},
  month = sep,
  howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/nomic-embed-text-v1-5/}},
  note = {United States of America Superintelligence. Last updated 2026-09-29}
}

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