---
license: apache-2.0
base_model: Alibaba-NLP/gte-modernbert-base
pipeline_tag: feature-extraction
library_name: onnx
tags:
- dakera
- ai-agent-memory
- agent-memory
- long-term-memory
- rag
- self-hosted
- mcp
- onnx
- mirror
---
gte-modernbert-base
ModernBERT embedding model for Dakera ยท a verified mirror by Dakera, the self-hosted memory engine for AI agents
> An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/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](https://github.com/Dakera-AI/dakera-deploy) 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](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 596.4 MB |
| `onnx/model_quantized.onnx` | [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 150.2 MB |
| `tokenizer.json` | [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) | 3.6 MB |
SHA-256 checksums
```text
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`](https://huggingface.co/DakeraAI/gte-modernbert-base/blob/main/LICENSE)). For what the model does, how it was trained and how to cite it, see the [original model card](https://huggingface.co/Alibaba-NLP/gte-modernbert-base).
## About Dakera
**[Dakera](https://dakera.ai) 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.
```bash
docker run -p 3000:3000 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest
```
| | |
|---|---|
| ๐ Website | [dakera.ai](https://dakera.ai) |
| ๐ Documentation | [dakera.ai/docs](https://dakera.ai/docs) |
| ๐ป GitHub | [github.com/Dakera-AI](https://github.com/Dakera-AI) |
| ๐ Self-hosting (Docker, Kubernetes, Helm) | [dakera-deploy](https://github.com/Dakera-AI/dakera-deploy) |
| ๐ MCP server | [dakera-mcp](https://github.com/Dakera-AI/dakera-mcp) |
| ๐งฐ SDKs | [Python](https://github.com/Dakera-AI/dakera-py) ยท [TypeScript](https://github.com/Dakera-AI/dakera-js) ยท [Go](https://github.com/Dakera-AI/dakera-go) ยท [Rust](https://github.com/Dakera-AI/dakera-rs) |
| ๐ผ LinkedIn | [Dakera AI](https://www.linkedin.com/company/dakera-ai) |
Questions or feedback: [amine@dakera.ai](mailto:amine@dakera.ai).