gte-modernbert-base

ModernBERT embedding model for Dakera · a verified mirror by Dakera, the self-hosted memory engine for AI agents

An unmodified, checksum-verified copy of the files Dakera uses from Alibaba-NLP/gte-modernbert-base. The model is by Alibaba-NLP; all credit goes to its authors.

In Dakera

A ModernBERT embedding model with a long context: DAKERA_MODEL=gte-modernbert-base.

Dakera fetches this embedding model from this repository first and from the original repositories if it is unreachable, so installs never depend on a single source. Both are checked against the same SHA-256, and the Dakera Docker image ships the default models ready to run, with no download at startup.

Files

Copied byte for byte from:

File	Source	Size
onnx/model.onnx	Alibaba-NLP/gte-modernbert-base	596.4 MB
onnx/model_quantized.onnx	Alibaba-NLP/gte-modernbert-base	150.2 MB
tokenizer.json	Alibaba-NLP/gte-modernbert-base	3.6 MB

SHA-256 checksums

947f31df7effaeec4edb57c50e4ed7e0f2034d9336063f92615b92e3e0d24d78  onnx/model.onnx
bae96b276d342bf86eeee07c1bdbc0c75bb82bf4033941aab7fabc1e33ee3b44  onnx/model_quantized.onnx
6c8aaa9a542084f2457eab775d4eeb51f92a70c0fd9de28d5edb0ddec3c08d30  tokenizer.json

Licence and credit

Apache-2.0, the same as the original model (see LICENSE). For what the model does, how it was trained and how to cite it, see the original model card.

About Dakera

Dakera is a self-hosted memory engine for AI agents. It gives agents long-term memory that survives sessions: hybrid vector and full-text retrieval, reranking, entity extraction and importance decay, in a single Rust binary that runs on your own servers, with an MCP server for Claude, Cursor and other agents.

docker run -p 3000:3000 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest

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