featherless-ai/simple-jev
- Source
- GitHub
- First trending
- Category
- Model inference
- GitHub stars
- 62
- Main language
- Python
This page introduces an external open-source repository. It is not an HDATF product.

What it does
Uses compatible open models for structured classification and scoring without training a separate classifier head. The server constructs JSON choices, rubric scores, or truth and support judgments from next-token logits rather than generated JSON text.
How it helps ATF
For Harness, the scoring interface may inform task-result checks. For LabChin, its structured classification and support judgments are worth comparing when designing research-material evaluation.
License
No license No license file found. Without a license, reuse of the code is not permitted by default.
More in this category
- bespokelabsai/nimble
A typed-decision model project sharing data curation, training, and serving methods. Given text and a question schema, it returns selected choices or true-or-false answers with probabilities for allowed answers. - hr98w/jev-visual
An educational Apple Silicon experiment using Qwen3.5 and MLX to answer multiple questions about one image by scoring candidate outputs. It includes a local browser interface, CLI and HTTP API. - NandhaKishorM/laya
A multilingual decision engine that answers typed questions about text, email, tickets, or JSON in a single forward pass. A router selects a checkpoint for each request. - anthropics/uplifting-biomolecular-modeling
A reference collection of inference optimization kits for protein and genomics machine-learning tools at pinned upstream versions. The release is not maintained and does not accept contributions. - dexmal/dexbotic
A PyTorch toolbox for vision-language-action research covering pretraining, fine-tuning, inference, and evaluation. It provides multiple model configurations, a unified robot training data format, and deployment scripts.
Only repositories in the ranked Trendshift lists are included, and the lists are used only to find candidates. We do not copy their ranks. Descriptions, licenses and star counts come from each GitHub repository. The notes are our own reading. We have not tested these projects, and a place on a trending list does not prove quality.