Back to directory

jev-curate

A Rust command-line tool that filters large synthetic and pretraining text datasets by sending each row through TypeSafe AI's Jev model for calibrated, rubric-based judgments instead of a slower general-purpose language model.

jev-curate

jev-curate is a dataset-curation tool for cleaning large volumes of synthetic reasoning data, instruction-tuning pairs or web-scraped text. It streams Parquet and JSONL data through TypeSafe AI's Jev model, sending every rubric question for a row in one request rather than one round trip per question, and returns typed choice, score and calibrated-probability judgments instead of free-form generated text.

It is distributed as a single Rust binary installed through Cargo, with a command-line interface that filters a dataset file against a named preset, such as one tuned for math reasoning, and writes the kept rows to an output directory. A Python API is also available. The project is an early beta release.

More in Models

See all 405
  • mini-AGI

    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.