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MODEL Listed

nomic-embed-text-v1.5

nomic-embed-text-v1.5 is a high-performance text embedding model designed for scalable retrieval and semantic search. Unlike many proprietary alternatives, it offers an open-weights approach under the Apache-2.0 license, making it an ideal choice for developers prioritizing data sovereignty and cost-efficiency. Its primary technical advantage is the support for Matryoshka embeddings, which allows developers to truncate vector dimensions without significant loss in accuracy, drastically reducing storage overhead and improving query latency in vector databases. Whether you are building a RAG pipeline, a recommendation engine, or a complex clustering system, this model provides a flexible, high-dimensional representation of text that integrates seamlessly into existing Python-based AI stacks.

nomic-aisentence similarity
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01 / MODEL CARD

Model card

nomic-embed-text-v1.5 is a high-performance text embedding model designed for scalable retrieval and semantic search. Unlike many proprietary alternatives, it offers an open-weights approach under the Apache-2.0 license, making it an ideal choice for developers prioritizing data sovereignty and cost-efficiency. Its primary technical advantage is the support for Matryoshka embeddings, which allows developers to truncate vector dimensions without significant loss in accuracy, drastically reducing storage overhead and improving query latency in vector databases. Whether you are building a RAG pipeline, a recommendation engine, or a complex clustering system, this model provides a flexible, high-dimensional representation of text that integrates seamlessly into existing Python-based AI stacks.

Model typesentence similarity
Providernomic-ai
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/nomic-ai/nomic-embed-text-v1.5
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Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: nomic-ai/nomic-embed-text-v1.5
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model nomic-ai/nomic-embed-text-v1.5
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model nomic-ai/nomic-embed-text-v1.5 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('nomic-ai/nomic-embed-text-v1.5')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/nomic-ai/nomic-embed-text-v1.5.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/nomic-ai/nomic-embed-text-v1.5.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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