tuantc/loupe-1.0

Loupe 1.0

A LoRA adapter for Qwen/Qwen3.5-4B that makes typed decisions about a document in one forward pass and returns calibrated probabilities over the options: a System-1 decision engine, fast judgment rather than generated reasoning. Served with the code at github.com/tuantong/loupe-serving, which reads the answer from the next-token probabilities of the option labels; nothing is generated.

Adapter

An exact average (model soup) of LoRA adapters on the attention, linear-attention and MLP projections of all 32 layers (three seeds of one recipe, a continuation of that recipe on written rationales, and a run whose targets include a second teacher), stored as one rank-96 adapter. Merge it into the base weights for serving. It was trained with the compact prompt template, so serve it with LOUPE_PROMPT_STYLE=compact. calibration.json reads answers to prompts of 600 tokens or more at temperature 0.7 and shorter ones at 1; the temperature was fitted on our own held-out cases, not on benchmark items.

Training (summary; training code and data are not released)

• Supervised targets on generated decision families (rules, judgments, routing, probability items with exact distributions) and on authored case files written for this project by DeepSeek V4.1 Flash.
• On authored cases, the targets are a larger model's distribution (Qwen3.8-27B) after reasoning, used only where that reasoning reached the verified answer; elsewhere the base model's own distribution.
• In one ingredient, where DeepSeek V4.1 Flash's stated distribution (after its own reasoning) put the verified answer first, the target is half the existing target and half that distribution.
• No output of a Claude or OpenAI model was used as a training case or target.
• No evaluation item or paraphrase was used. Every authored case was checked for shared passages with the benchmark's public items and dropped when it had one.

Evaluation (our runs, not official scores)

Public tiers of a System-1 decision benchmark (typed decisions over documents, many with a tempting wrong reading):

Tier	Items	Accuracy
Easy	48	100.0%
Standard	72	100.0%
Hard	111	78.4%

Hard-tier ECE 0.060; mean total variation distance to the exact distributions of the probability items 0.162. Serial loopback latency on an RTX PRO 6000 Blackwell with the CUDA-graph backend and the adapter merged: p50 13.9 ms, p95 15.8 ms.

Limitations

English only. It decides from what the document says and does not look anything up. Temporal arithmetic over long documents, ranked-rule tradeoffs and long policies remain its weakest families.

Licence

Apache-2.0, like the base model (Qwen/Qwen3.5-4B) and the serving code. The training data was written by DeepSeek V4.1 Flash, with targets from Qwen3.8-27B (Apache-2.0) and DeepSeek V4.1 Flash.