colmodernvbert

Image and document-page retrieval 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 ModernVBERT/colmodernvbert. The model is by ModernVBERT; all credit goes to its authors.

In Dakera

Makes images and document pages searchable by text: DAKERA_VISION=true.

Dakera fetches this vision retrieval 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
model.onnx	Qdrant/colmodernvbert	1.0 GB
tokenizer.json	Qdrant/colmodernvbert	3.6 MB

SHA-256 checksums

b795e84e70ca6fc549f59c22a5d87e785bd741ce73a74bacfaf0caa967b546ab  model.onnx
948b2ac5b46b1890f2fa4c43f41add4a6c04432e9cf529d788d1d7b10a3dea00  tokenizer.json

Licence and credit

MIT, 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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