| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.5-4B |
| library_name: peft |
| pipeline_tag: text-classification |
| tags: |
| - lora |
| - decision |
| - calibration |
| - system-1 |
| --- |
| |
| # Loupe 1.0 |
|
|
| A LoRA adapter for [Qwen/Qwen3.5-4B](https://huggingface.co/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](https://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. |
|
|
