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license: mit
base_model: ModernVBERT/colmodernvbert
pipeline_tag: visual-document-retrieval
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">colmodernvbert</h1>
<p align="center"><b>Image and document-page retrieval 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/colmodernvbert/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/ModernVBERT/colmodernvbert"><img alt="Original: ModernVBERT/colmodernvbert" src="https://img.shields.io/badge/Original-ModernVBERT%2Fcolmodernvbert-6b7280?style=flat-square"></a>
</p>
> An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [ModernVBERT/colmodernvbert](https://huggingface.co/ModernVBERT/colmodernvbert). The model is by **ModernVBERT**; all credit goes to its authors.
## In Dakera
Makes images and document pages searchable by text: `DAKERA_VISION=true`.
Dakera fetches this vision retrieval 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 |
|---|---|---|
| `model.onnx` | [Qdrant/colmodernvbert](https://huggingface.co/Qdrant/colmodernvbert) | 1.0 GB |
| `tokenizer.json` | [Qdrant/colmodernvbert](https://huggingface.co/Qdrant/colmodernvbert) | 3.6 MB |
<details>
<summary>SHA-256 checksums</summary>
```text
b795e84e70ca6fc549f59c22a5d87e785bd741ce73a74bacfaf0caa967b546ab model.onnx
948b2ac5b46b1890f2fa4c43f41add4a6c04432e9cf529d788d1d7b10a3dea00 tokenizer.json
```
</details>
## Licence and credit
MIT, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/colmodernvbert/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/ModernVBERT/colmodernvbert).
## 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).
|