|
Download README.md from DakeraAI/e5-small-v2: direct link, hf CLI and curl.
- Browser
- Download file 4.62 kB
-
https://huggingface.co/DakeraAI/e5-small-v2/resolve/main/README.md
- Command line
-
hf download hf://DakeraAI/e5-small-v2/README.md
-
curl -L -o README.md https://huggingface.co/DakeraAI/e5-small-v2/resolve/main/README.md
4.62 kB
| 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 | |
| <p align="center"> | |
| <a href="https://dakera.ai"><img src="https://dakera.ai/assets/logo-128.png" width="96" alt="Dakera logo"></a> | |
| </p> | |
| <h1 align="center">e5-small-v2</h1> | |
| <p align="center"><b>Compact English embedding model for Dakera 路 a verified mirror by Dakera, the self-hosted memory engine for AI agents</b></p> | |
| <p align="center"> | |
| <a href="https://dakera.ai"><img alt="Dakera: dakera.ai" src="https://img.shields.io/badge/Dakera-dakera.ai-c9a227?style=flat-square"></a> <a href="https://dakera.ai/docs"><img alt="Docs: dakera.ai/docs" src="https://img.shields.io/badge/Docs-dakera.ai%2Fdocs-0b0b0b?style=flat-square"></a> <a href="https://github.com/Dakera-AI"><img alt="GitHub: Dakera-AI" src="https://img.shields.io/badge/GitHub-Dakera--AI-181717?style=flat-square&logo=github&logoColor=white"></a> <a href="https://huggingface.co/DakeraAI/e5-small-v2/blob/main/LICENSE"><img alt="License: MIT" src="https://img.shields.io/badge/License-MIT-2b6cb0?style=flat-square"></a> <a href="https://huggingface.co/intfloat/e5-small-v2"><img alt="Original: intfloat/e5-small-v2" src="https://img.shields.io/badge/Original-intfloat%2Fe5--small--v2-6b7280?style=flat-square"></a> | |
| </p> | |
| > 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 | | |
| <details> | |
| <summary>SHA-256 checksums</summary> | |
| ```text | |
| b2a43b66f7f9b6f29643a21340ad7c03d2d91e6bd5d43429a77799a9ee880eb0 onnx/model.onnx | |
| 7d9092cb25f2bd1c023b7e8d2aa459044a02030ac880e5a59fdaf27af69f1ded onnx/model_quantized.onnx | |
| 5dfb0363cd0243be179c03bcaafd1542d0fbb95e8cbcf575fff3e229342adc2f config.json | |
| 45bfa60070649aae2244fbc9d508537779b93b6f353c17b0f95ceccb1c5116c1 model.safetensors | |
| d241a60d5e8f04cc1b2b3e9ef7a4921b27bf526d9f6050ab90f9267a1f9e5c66 tokenizer.json | |
| ``` | |
| </details> | |
| ## 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 | |
| ``` | |
| | | | | |
| |---|---| | |
| | 馃寪 Website | [dakera.ai](https://dakera.ai) | | |
| | 馃摎 Documentation | [dakera.ai/docs](https://dakera.ai/docs) | | |
| | 馃捇 GitHub | [github.com/Dakera-AI](https://github.com/Dakera-AI) | | |
| | 馃殌 Self-hosting (Docker, Kubernetes, Helm) | [dakera-deploy](https://github.com/Dakera-AI/dakera-deploy) | | |
| | 馃攲 MCP server | [dakera-mcp](https://github.com/Dakera-AI/dakera-mcp) | | |
| | 馃О SDKs | [Python](https://github.com/Dakera-AI/dakera-py) 路 [TypeScript](https://github.com/Dakera-AI/dakera-js) 路 [Go](https://github.com/Dakera-AI/dakera-go) 路 [Rust](https://github.com/Dakera-AI/dakera-rs) | | |
| | 馃捈 LinkedIn | [Dakera AI](https://www.linkedin.com/company/dakera-ai) | | |
| Questions or feedback: [amine@dakera.ai](mailto:amine@dakera.ai). | |