---
title: DataScience-UIBK/uibk-embedding-v1
url: https://recentmodel.com/models/DataScience-UIBK/uibk-embedding-v1/
status: archived · low-signal
published: 2026-10-09T23:08:24Z
updated: 2026-10-20T11:19:23Z
source: https://huggingface.co/DataScience-UIBK/uibk-embedding-v1
---

# DataScience-UIBK/uibk-embedding-v1

UIBK-Embedding does it inside one model: - one encoder pass produces the token vectors and the dense embedding, - the dense embedding is stored as three extra vectors in the same list as the token vectors, - a plain MaxSim over that list is the sum of the two scores. Dense, 149M params. Context: 8K tokens (config). License: apache-2.0. Vendor-reported: "Parameters: 179M (shared trunk and token top 149.0M, dense top 20.1M, output layer 9.9M)"

- Vendor-reported: Parameters: 179M (shared trunk and token top 149.0M, dense top 20.1M, output layer 9.9M)

- **Org:** DataScience-UIBK
- **Released:** 2026-10-09
- **Status:** archived · low-signal
- **License:** apache-2.0 (permissive)
- **Parameters:** 149M
- **Architecture:** dense · ModernBertModel
- **Context:** 8,192 tokens
- **Task:** sentence-similarity · embedding
- **Signal:** 9 HF likes · 1111 downloads
- **Archived:** 2026-10-20 (no longer listed by the collector; stats as last seen)

## Runs on

estimate from params; sizing calculation, not tested.

- Q4 ~1.7 GB (estimated): no listed device
- Q8 ~1.8 GB (estimated): no listed device
- FP16 ~1.9 GB (estimated): 8GB GPU (4060 / 3070), 12GB GPU (RTX 3060), 16GB GPU (5060 Ti / 4060 Ti / 4080 / 5080), 24GB GPU (3090 / 4090), 32GB GPU (5090), 48GB GPU (RTX 6000 Ada / A6000), 96GB GPU (RTX Pro 6000 Blackwell), 1× DGX Spark (128GB), 2× DGX Spark (256GB), 16GB GPU (7800 XT / 9070 XT), 24GB GPU (7900 XTX), Strix Halo 128GB unified (~96GB usable), Arc B60 24GB, Arc B70 32GB, Mac 16GB, Mac 24GB, Mac 36GB, Mac 48GB, Mac 64GB (M1 Max), Mac 96GB, Mac 128GB, Mac 192GB, Mac 256GB, Mac 512GB, CPU only, 32GB RAM (slow), CPU only, 64GB RAM (slow), CPU only, 128GB RAM (slow), CPU only, 256GB RAM (slow)

## Links

- [Hugging Face](https://huggingface.co/DataScience-UIBK/uibk-embedding-v1)