tuantc

loupe-1.0

README

License: apache-2.0

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):

Table with columns: Tier, Items, Accuracy
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.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

tuantc

Model Tree

Base

Qwen/Qwen3.5-4B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

Adapter

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):

Table with columns: Tier, Items, Accuracy
Tier	Items	Accuracy
Easy	48	100.0%
Standard	72	100.0%
Hard	111	78.4%

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.