---
language:
- en
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: feature-extraction
base_model:
- BAAI/bge-m3
datasets:
- Cilow/Vinci-Evals
tags:
- sentence-transformers
- text-embeddings-inference
- embeddings
- retrieval
- matryoshka
- lattice
- cilow
- db-native
- claim-aware
---

# Vinci

Vinci is Cilow's DB-native, claim-aware embedding model family for the Lattice
retrieval pipeline. This repo is the canonical Hub target for `Cilow/Vinci`.

Current status: **experiment-runner scaffold**. Do not treat this repo as production-ready until
the shadow evaluation report shows no regression against the current production
baseline and the repo contains trained model weights.

## Model Contract

- Model id: `Cilow/Vinci`
- Legacy aliases: `cilow/vinci-v0, cilow/cilow-embed-v0`
- Base model: `BAAI/bge-m3`
- Full dimension: `1024`
- Low-dimension Matryoshka prefix: `256`
- Similarity: cosine
- Serializer: `claim_v1`
- Pipeline: `Lattice`
- Fresh index required when switching semantic spaces: yes

## Intended Use

Vinci is designed for retrieval over structured application context: factual
claims, memories, code snippets, tool traces, database rows, temporal updates,
provenance, confidence, and supersession state. It is not a general-purpose
similarity model by default; promotion depends on Cilow/Lattice retrieval gates.

## Training Plan

The first training run fine-tunes `BAAI/bge-m3` with
`SentenceTransformerTrainer`, `MultipleNegativesRankingLoss`, and
`MatryoshkaLoss([1024, 256])`. The dataset policy is synthetic plus explicit
eval fixtures only. Private user memories are excluded.

## Data Policy

- Private user data: `False`
- Allowed sources: synthetic and checked evaluation fixtures
- LongMemEval-like data: eval-only unless an explicit train/dev/test split is approved
- Hard negatives: superseded facts, wrong predicate/entity, numeric near misses,
  temporal stale/current conflicts, and semantic neighbors with factual mismatch

## CPU Smoke Metrics

| Backend | Dim | Recall@5 | Recall@10 | MRR@10 | nDCG@10 |
|---|---:|---:|---:|---:|---:|
| deterministic | 256 | 0.1429 | 0.7143 | 0.1160 | 0.2484 |
| deterministic | 1024 | 0.2857 | 0.7143 | 0.1262 | 0.2581 |

These CPU metrics use the deterministic backend unless otherwise stated. They
are a pipeline smoke test, not a production quality claim.

## Dataset Splits

| Split | Queries | Triplets |
|---|---:|---:|
| test | 2 | 12 |
| train | 5 | 30 |

## Deployment

The preferred shadow path is TEI with `TEI_MODEL=Cilow/Vinci` and
`TEI_DIMENSION=1024`. Local inference uses the ONNX lane only after export and
dimension validation pass. Lattice must encode model id, serializer version,
dimension, corpus version, and index version in metadata to prevent mixed-vector
comparisons.

## Limitations

- No private data is included in the starter dataset.
- A trained checkpoint must still pass local SentenceTransformer eval, TEI eval,
  and a production-shadow comparison before promotion.
- Voyage or the current production provider remains default until Vinci matches
  or beats the baseline without latency regression.

Generated: `2026-05-02T02:51:25+00:00`