Simple Jev turns Hugging Face models into classifier APIs

Featherless AI has released Simple Jev repository, an Apache License 2.0 library that wraps compatible Hugging Face language models in a structured classifier API. The project provides an open implementation of the interface offered by TypeSafe AI’s closed-weight Jev service.

TypeSafe Jev accepts context and returns typed decisions with confidence scores. TypeSafe lists a price of $0.042 per million input tokens, free output tokens, and response times from 70 to 500 milliseconds. Simple Jev exposes a similar contract through models such as Qwen and Gemma, with options for self-hosting, a free public demo, and a hosted Featherless endpoint.

Structured output without generation

Simple Jev derives answers from the model’s next-token logits, which represent its scores for possible next tokens. A request contains shared context and one or more questions with allowed labels. The server compares the logits for those labels and constructs the JSON response from their probabilities.

During inference, the model performs a prefill pass over the prompt and scores a small set of allowed answer tokens, such as red and blue. The request requires no autoregressive decode loop, schema-constrained sampling, or parsing of generated JSON. For choice questions, the response includes the selected label, its confidence score, and the probability distribution across candidates.

The API supports three question types: