Vinci MLE: AI for machine-learning engineering

Help with the work of building AI

Vinci MLE is trained to read files and experiment results, suggest a limited fix when evidence supports it, preserve a working setup when it does not, and identify missing evidence instead of guessing.

ML engineers and AI researchers can evaluate it on experiment diagnosis, training recovery, memory and batch settings, scoring checks, model packaging and configuration consistency. These describe intended workflows and training coverage, not demonstrated performance on new tasks.

SimpleDirect® specializes existing IBM Granite and Mistral Devstral foundations through post-training. These releases are not foundation models pretrained from scratch in Canada.

Separate training intent from evaluation evidence

Valid held-out ML-engineering and external MLE-bench evaluations were not completed for 1.0. General-capability comparisons with each model’s own parent document measured gains and tradeoffs, but do not establish performance on new ML-engineering tasks.

Use each artifact’s own evaluation record. Results for a parent model, sibling size or full-precision checkpoint do not establish the performance of a different release or quantized format.

Plan a self-hosted deployment

Start with a bounded experiment, a baseline and explicit acceptance criteria. Download full weights or a supported GGUF format from the model card, then evaluate suggestions against the original experiment evidence before permitting file edits or commands.

Allow memory beyond the weight files, choose a supported runtime, and record the exact revision and format. Tool use requires your own application, permissions and controlled workspace. Review and test suggested changes before applying them; these pages do not offer hosted inference.