| --- |
| license: apache-2.0 |
| base_model: sentence-transformers/all-MiniLM-L6-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">all-MiniLM-L6-v2</h1> |
|
|
| <p align="center"><b>Lightweight 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/all-MiniLM-L6-v2/blob/main/LICENSE"><img alt="License: Apache-2.0" src="https://img.shields.io/badge/License-Apache--2.0-2b6cb0?style=flat-square"></a> <a href="https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"><img alt="Original: sentence-transformers/all-MiniLM-L6-v2" src="https://img.shields.io/badge/Original-sentence--transformers%2Fall--MiniLM--L6--v2-6b7280?style=flat-square"></a> |
| </p> |
|
|
|
|
| > An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). The model is by **sentence-transformers**; all credit goes to its authors. |
|
|
| ## In Dakera |
|
|
| The lightest embedding model Dakera supports, for small hosts: `DAKERA_MODEL=minilm`. |
|
|
| 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/all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) | 90.4 MB | |
| | `onnx/model_quantized.onnx` | [Xenova/all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) | 23.0 MB | |
| | `config.json` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 612 B | |
| | `model.safetensors` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 90.9 MB | |
| | `tokenizer.json` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 466.2 kB | |
|
|
| <details> |
| <summary>SHA-256 checksums</summary> |
|
|
| ```text |
| 759c3cd2b7fe7e93933ad23c4c9181b7396442a2ed746ec7c1d46192c469c46e onnx/model.onnx |
| afdb6f1a0e45b715d0bb9b11772f032c399babd23bfc31fed1c170afc848bdb1 onnx/model_quantized.onnx |
| 953f9c0d463486b10a6871cc2fd59f223b2c70184f49815e7efbcab5d8908b41 config.json |
| 53aa51172d142c89d9012cce15ae4d6cc0ca6895895114379cacb4fab128d9db model.safetensors |
| be50c3628f2bf5bb5e3a7f17b1f74611b2561a3a27eeab05e5aa30f411572037 tokenizer.json |
| ``` |
|
|
| </details> |
|
|
| ## Licence and credit |
|
|
| Apache-2.0, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/all-MiniLM-L6-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/sentence-transformers/all-MiniLM-L6-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). |
|
|
