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CommunityPractices & Patterns3.6k starsVerified 2026-09-22

SemIf

An independent baseline that reads typed option probabilities straight from a frozen Qwen3.5-4B's logits in one forward pass, reproducing Jev's interface pattern with open models rather than Jev's undisclosed model or training.

Category
Practices & Patterns
Published by
Community
Author
TheoLeeCJ
Added
2026-09-22
Tagscommunitypythonopen-modelsbenchmarkscalibration

Highlights

  • On one RTX 3090, direct typed logits answered 21 binary criteria in 1.023 s versus 5.332 s and 111 output tokens for an autoregressive JSON array.
  • Backends cover CUDA Torch, CPU llama.cpp from GGUF, and MLX or PyTorch MPS on Apple Silicon, with prompt hashes matching row for row.
  • A WebGPU browser demo runs MiniCPM5 2B and Qwen3.5 4B locally with no waitlist or install.
  • Shared mode prefills one long state once and branches it across many criteria in parallel.
  • The fixture, exact runner commands, row-level outputs, model revisions, and known failures are committed for audit.

Quickstart

bash
python -m venv .venv
. .venv/bin/activate
pip install -e '.[test]'
CUDA_VISIBLE_DEVICES=0 semif-score --mode direct --model Qwen/Qwen3.5-4B --input examples/decisions.jsonl --output results.jsonl

Watch out

MIT-licensed. Not affiliated with TypeSafe; needs Python 3.10+ and a CUDA GPU holding a 4B BF16 model (or a local GGUF for the CPU backend), weights are not included, and scores depend on the quantized weights.

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