# Maincode: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/maincode
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Models

Model · Feature extraction

### [matilda-jev-v1](https://savrn.com/models/matilda-jev-v1)

[Maincode](https://savrn.com/model-publishers/maincode)

Matilda-Jev is Maincode's one-pass decision model. It scores the options supplied in a choice, noul (yes/no), or ordered score question. It accepts text or JSON state and optional images. This is a decision checkpoint with a 255-option readout, not a text-generation checkpoint. This configuration edition uses MatildaJevModel, MatildaJevConfig, and MATILDA tokenizer/processor classes. Use the bundled runtime below, or load the custom AutoClasses with trustremotecode=True. The package includes the backbone weights, decision readout, tokenizer, preprocessing configuration and calibrated temperature. On AMD MI355X with Python 3.12, Transformers 5.17.0 and PyTorch 2.14.0: - 25/25 smoke-test…

Open weights apache-2.0 26.1B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/matilda-jev-v1)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-10-07.
- [Hugging Face profile](https://huggingface.co/Maincode)
- [How the hub is built](https://savrn.com/model-hub/methodology)