---
license: mit
base_model: intfloat/e5-small-v2
pipeline_tag: feature-extraction
library_name: onnx
tags:
- dakera
- ai-agent-memory
- agent-memory
- long-term-memory
- rag
- self-hosted
- mcp
- onnx
- mirror
---
e5-small-v2
Compact English 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 [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2). The model is by **intfloat**; all credit goes to its authors.
## In Dakera
A small English embedding model: `DAKERA_MODEL=e5-small`.
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` | [Xenova/e5-small-v2](https://huggingface.co/Xenova/e5-small-v2) | 133.1 MB |
| `onnx/model_quantized.onnx` | [Xenova/e5-small-v2](https://huggingface.co/Xenova/e5-small-v2) | 34.0 MB |
| `config.json` | [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) | 615 B |
| `model.safetensors` | [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) | 133.5 MB |
| `tokenizer.json` | [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) | 711.4 kB |
SHA-256 checksums
```text
b2a43b66f7f9b6f29643a21340ad7c03d2d91e6bd5d43429a77799a9ee880eb0 onnx/model.onnx
7d9092cb25f2bd1c023b7e8d2aa459044a02030ac880e5a59fdaf27af69f1ded onnx/model_quantized.onnx
5dfb0363cd0243be179c03bcaafd1542d0fbb95e8cbcf575fff3e229342adc2f config.json
45bfa60070649aae2244fbc9d508537779b93b6f353c17b0f95ceccb1c5116c1 model.safetensors
d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66 tokenizer.json
```
## Licence and credit
MIT, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/e5-small-v2/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/intfloat/e5-small-v2).
## 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).