Model card
nomic-embed-text-v1 is a high-performance text embedding model designed for developers building RAG pipelines and semantic search engines. Unlike many proprietary alternatives, it offers a massive 8192-token context window, allowing you to embed long-form documents without aggressive chunking that destroys semantic meaning. It is specifically optimized for sentence similarity and retrieval tasks, providing a competitive balance between vector dimensionality and retrieval accuracy. With an Apache-2.0 license, it provides the flexibility for commercial deployment across various infrastructure stacks, making it a robust open-source alternative to OpenAI's embedding suite for those prioritizing data sovereignty and cost efficiency.
Model files and versions
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.
nomic-ai/nomic-embed-text-v1Install the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model nomic-ai/nomic-embed-text-v1README.md is used as an example; replace it with another repository file when needed.
modelscope download --model nomic-ai/nomic-embed-text-v1 README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('nomic-ai/nomic-embed-text-v1')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/nomic-ai/nomic-embed-text-v1.gitFetch 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.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
Discussions
Use this space to keep checking source information, usage experience and maintenance status.
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