Bundle the qevi inference engine in-repo

qevi/__init__.py

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+"""Qevi: answer typed questions about an image in one forward pass, without generating text.
+
+A normal vision-language model answers a question by decoding tokens one at a time, and
+re-encodes the image for every question you ask. When the answer is known to be one of a small
+fixed set ("Yes"/"No", "A"/"B"/"C"/"D", "Poor"/"Fair"/"Good"), both of those costs are waste.
+
+Qevi instead encodes the image once, packs every question into a single sequence behind a
+block-diagonal attention mask that keeps the questions provably isolated from each other, runs
+one forward pass, and reads the model's own LM-head logits at the position where it would have
+started writing each answer. Softmax over just that question's candidate tokens gives a
+calibrated probability.
+
+    from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+    from qevi import build_readouts, noul_question, forward_logits, softmax
+
+    model     = Qwen3VLForConditionalGeneration.from_pretrained(MODEL, dtype="bfloat16").cuda()
+    processor = AutoProcessor.from_pretrained(MODEL)
+
+    specs    = [(noul_question("There is a ladder in this image."), ["Yes", "No"])]
+    readouts = build_readouts(processor.tokenizer, specs)
+    logits   = forward_logits(model, processor, image, readouts)
+    print(softmax(logits[0], T=2.45))     # -> [0.97, 0.03]
+
+Everything here works with a stock Qwen3-VL checkpoint. The fine-tuned weights are simply more
+accurate through the same path; no custom architecture and no trust_remote_code is involved.
+"""
+
+from .core import (
+    Readout,
+    build_readouts,
+    forward_logits,
+    softmax,
+    unwrap,
+    wrap_turn,
+    noul_question,
+    choice_question,
+    choice_question_generic,
+    score_question,
+    score_question_generic,
+    NOUL_TEMPLATE,
+    LETTERS,
+    PREFIX_PRE,
+    PREFIX_POST,
+)
+from .pack import Question, PackedBatch, Segment, build_packed_batch, permute_questions
+
+__version__ = "0.1.0"
+
+DEFAULT_TEMPERATURES = {"noul": 2.45, "choice": 1.9, "score": 3.0}
+
+__all__ = [
+    "Readout", "build_readouts", "forward_logits", "softmax", "unwrap", "wrap_turn",
+    "noul_question", "choice_question", "choice_question_generic",
+    "score_question", "score_question_generic",
+    "NOUL_TEMPLATE", "LETTERS", "PREFIX_PRE", "PREFIX_POST",
+    "Question", "PackedBatch", "Segment", "build_packed_batch", "permute_questions",
+    "DEFAULT_TEMPERATURES", "__version__",
+]