Vinci Technical Report No. 3
Runtime Pass Is Not Correctness
A failed training recipe—and what auditing its evaluator revealed.
Read the technical reportVinci Labs
Research that shows its work.
Models, experiments, and the questions that move our work forward.
Findings, written up to be checked
Vinci Technical Report No. 2
Character Transfer Across Three Model Families
A three-family study of behavioural gains and capability costs.
Read the technical reportVinci Technical Report No. 1
Transferring Character Post-Training to Mistral 7B
Testing whether character training transfers to a new model family.
Read the technical reportFrom research to open weights.
Meet Piccolo, Bozza, and the models that shaped Vinci.
Model downloads and limitations
Vinci-Prova-7B-1.0
7BExperimental- Base
- mistralai/Mistral-7B-Instruct-v0.3 @ c170c708c41dac9275d15a8fff4eca08d52bab71
- Licence
- Apache-2.0
Vinci-Bozza-1.0
9B- Base
- Qwen/Qwen3.5-9B
- Licence
- Apache-2.0
Vinci-Piccolo-1.0
4B- Base
- Qwen/Qwen3.5-4B
- Licence
- Apache-2.0
Go deeper.
All research and release records
Vinci ProvaResearch report
Runtime pass is not correctness
A conservative SFT+DPO recipe missed every reasoning-efficiency target — and auditing the evaluator showed the original bank accepted 24 of 24 deliberately incorrect programs.
Read the articleVinci ProvaResearch report
We tested one character recipe across three model families. None met the bar.
Unsupported assertions fell in all three families under both judges — but every family lost too much grounded-answer accuracy to meet the pre-registered bar.
Read the articleVinci ProvaResearch report
We tested whether character post-training transferred to Mistral 7B. It did — with trade-offs.
It transferred to Mistral — development-set model-judged fabrication fell from 53.8% to 8.6% — but it cost 5.6 points of GSM8K, and the model now sometimes refuses ordinary work.
Read the articleVinci FlagshipPost-mortem
We trained a model to be more honest. It learned to be shorter.
The honesty gain was explained by output length — and the measurement that produced it was disqualified.
Read the articleVinci FlagshipModel release
Introducing Vinci Bozza
Adversarial attack success reached 0.0% and citation integrity rose, at the cost of 10.7 points of instruction-following — and three flattering benchmark jumps were thrown out as extraction artifacts.
Read the articleVinci FlagshipModel release
Introducing Vinci Piccolo
The first 4B mainline release: strong safety numbers and solid general ability for its size, with tool-calling at 23.0%.
Read the articleA closer look at the measurements
We tested whether character post-training transferred to Mistral 7B. It did — with trade-offs.
It transferred to Mistral — development-set model-judged fabrication fell from 53.8% to 8.6% — but it cost 5.6 points of GSM8K, and the model now sometimes refuses ordinary work.
Read the article| Measured result | Base model | After training | Change |
|---|---|---|---|
| Judged fabricationlower is better | 53.8% | 8.6% | -45.2 percentage points |
| GSM8Khigher is better | 51.6% | 46.0% | -5.6 percentage points |
How we publish
- The question comes before the headline.
- Success and failure criteria are set against a named baseline, not chosen after the numbers land.
- Gains are reported next to their costs.
- Measurements that failed or were disqualified stay in the record, marked unusable, with the reason.
- Limitations are stated, not implied.
- Provenance, versions, and pinned revisions are preserved.
- Artifacts are released where it is responsible to, with the license and access state named.
- When a finding changes a mainline model, the record says what changed.
Newsroom
Introducing Vinci Cyber: Open-Weight Models for Defensive Cybersecurity
Vinci released Vinci Cyber, a family of three open-weight models built for defensive cybersecurity: 8B, 30B, and 123B parameters.
By George Pu
Research
We tested one frozen character recipe across three model families. None met the bar.
We applied one frozen character post-training recipe to Qwen3, Ministral, and OLMo. Unsupported assertions fell in every family, but grounded-answer accuracy fell too far in every family. We are publishing the failure, not a model checkpoint.
By George Pu
Research
Beyond fine-tuning: what Vinci is becoming
Fine-tuning was enough to change our models' voice. It was not enough to build the models we now want to release. We are moving from light behaviour tuning to full-weight model redevelopment - open foundations, rebuilt by Vinci. Here is the plan, stage by stage.
By George Pu