---
title: Daecore
sdk: static
emoji: 📚
colorFrom: gray
colorTo: blue
---

# Small models for local search and passage labeling

Daecore's models find and organize evidence in working notes and technical documentation. They run locally through ONNX Runtime, without a hosted model API, and each package can be used on its own.

| Model | Use it to | Returns | License |
|---|---|---|---|
| [EmbeddingGemma 300M v2](https://huggingface.co/Daecore/embeddinggemma-300m-memory-ft-v2) | Find passages related to a query | A normalized 768-dimensional vector per query or passage | Gemma Terms of Use |
| [Ettin 150M](https://huggingface.co/Daecore/ettin-150m-memory-reranker-ft-v1) | Put the most useful candidates first | A relevance score per query–passage pair | Apache-2.0 |
| [GLiClass Knowledge Classifier v2](https://huggingface.co/Daecore/gliclass-knowledge-classifier-v2) | Label what a passage contains | Scores for trap, decision, constraint, mechanism and procedure | Apache-2.0 |

**Retrieval.** Gemma and Ettin form Daecore's retrieval pipeline. Gemma and BM25 keyword search retrieve candidates, reciprocal-rank fusion merges their rankings, and Ettin reranks up to 50 unique passages. A calibrated selector then returns 3–20 of them when enough candidates exist.

Passages are prepared before retrieval: deterministic structural chunking
follows headings and natural boundaries, carries section context, and keeps
code and table framing where possible. Token-aware splitting bounds oversized
material. Training uses frozen snapshots of these passages, including earlier
parser outputs; changing the chunker changes the evidence available to search.
Results are source passages for an agent to read and use, and several relevant
passages can repeat the same fact.

**Passage labeling.** GLiClass serves a separate Daecore function: labeling what a passage contains, so stored material can be organized by kind. It is not a retrieval component and needs neither retrieval model; its labels do not judge relevance, truth or authority.

Each card has a runnable example near the top, its training recipe and a comparison with the exact upstream checkpoint on Daecore's evaluation data. Gemma also reports five public retrieval datasets and the storage/quality tradeoff of smaller embeddings; Ettin reports two public reranking datasets. Their pipeline tables change one model at a time against the same fully fine-tuned system, showing what each contributes alongside BM25.

Task-specific training and evaluation rely on mostly generated organizational documents and model-generated labels, without a human-adjudicated reference, so reported gains describe that data rather than a general improvement over the upstream models. Serving checks ran on one Windows x64 machine with an NVIDIA RTX 3060 Ti; Linux and AMD or Intel GPUs are untested.