AI product Open source
SemIf is an independent open-source tool, formerly called OpenJev, for making semantic if-statements with open language models. It accepts unstructured state, runtime-defined questions or criteria, and typed options, then reads the model’s option logits directly to return conditional probabilities without sampling an answer sentence, parsing JSON, or running a decoding loop. It supports direct, serial, and shared-state execution, prefilling common state to evaluate multiple criteria in parallel. Results and workflows include option scores, calibration, replay, benchmarks, verification, prompt hashes, model revisions, timings, and row-level outputs. It supports CUDA and CPU execution, Apple Silicon through MLX or PyTorch/MPS, and local GGUF models through llama.cpp. It includes an interactive browser WebGPU demo, reproducible fixtures, benchmark runners, and quality evaluations, and is released under the MIT License. SemIf is not affiliated with TypeSafe or Jev.
3 uses taken from transcripts — each links to the moment in the video.
Reads option-token probabilities directly from open models to make fast choices without generating and parsing JSON.
A project formerly called OpenJev that makes runtime-defined semantic decisions using open models on a single home GPU. It takes unstructured state, a question, and typed options, then returns option probabilities without generating answer text.
Turns open language models into semantic if-statements that score predefined options directly instead of generating text to parse. It supports shared documents, CUDA, Apple Silicon, and CPU backends, with test inputs and results included.
3 in the library.