bge-m3

Multilingual 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 BAAI/bge-m3. The model is by BAAI; all credit goes to its authors.

In Dakera

Embeddings for about 100 languages and long documents: DAKERA_MODEL=bge-m3.

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
tokenizer.json	BAAI/bge-m3	17.1 MB
onnx/model_quantized.onnx	Xenova/bge-m3	569.7 MB

SHA-256 checksums

21106b6d7dab2952c1d496fb21d5dc9db75c28ed361a05f5020bbba27810dd08  tokenizer.json
0826f8c1ab9edf1801db86c61919d4d108e8bfc0b809ec823ad366882ff0b77d  onnx/model_quantized.onnx

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

🌐 Website	dakera.ai
📚 Documentation	dakera.ai/docs
💻 GitHub	github.com/Dakera-AI
🚀 Self-hosting (Docker, Kubernetes, Helm)	dakera-deploy
🔌 MCP server	dakera-mcp
🧰 SDKs	Python · TypeScript · Go · Rust
💼 LinkedIn	Dakera AI

Questions or feedback: amine@dakera.ai.