---
base_model: unsloth/Qwen3.5-4B
library_name: peft
license: apache-2.0
tags:
  - law
  - trademark
  - abercrombie
  - legalbench
  - unsloth
  - decision-model
  - clef
  - lora
---

# Abercrombie-Decision

An [Unsloth](https://github.com/unslothai/unsloth) **decision model** that classifies trademarks on the
Abercrombie distinctiveness spectrum (Generic / Descriptive / Suggestive / Arbitrary / Fanciful). It is a
LoRA adapter on `unsloth/Qwen3.5-4B` plus a Clef-style decision head.

It does not generate text. The LLM reads the mark once, and the head scores the options for six questions
together: five yes/no doctrine sub-questions and a final 5-level distinctiveness score. Every answer comes
with a probability.

This is a proof of concept and the companion to [DoodDood/abercrombie-grpo](https://huggingface.co/DoodDood/abercrombie-grpo).
It uses the same base model, training marks and test set, with a different training method.

## Results

On the **95-row LegalBench Abercrombie** test set. Training data excluded every test mark.

| Category    | Base Qwen3.5-4B | Abercrombie-GRPO | **This model** (seed 42) | Mean of 3 seeds |
|-------------|:---------------:|:----------------:|:------------------------:|:---------------:|
| Generic     | 89%             | 100%             | **100%**                 | 100%            |
| Descriptive | 100%            | 74%              | **95%**                  | 95%             |
| Suggestive  | 5%              | 26%              | **26%**                  | 25%             |
| Arbitrary   | 0%              | 47%              | **68%**                  | 65%             |
| Fanciful    | 5%              | 95%              | **89%**                  | 91%             |
| **Overall** | 40.0%           | 68.4%            | **75.8%**                | **75.1% +/- 1.2** |

Mean ordinal distance: **0.34** (GRPO 0.53, base 1.09). Training took **about 7 minutes** on one RTX PRO 6000 (Colab G4).

Sub-question accuracy (seed 42): coined 98%, semantic link 93%, imagination 82%, conveys 81%, genus 95%. The
sub-answers, run through the routing rule below, agree with the model's own final answer on 94% of marks.

A single-question variant (distinctiveness only) scored 74.4% +/- 3.7, which is not a significant difference
(paired sign test p = 0.50).

## Limitations (read before using)

- **Small test set.** 95 rows means each run's accuracy carries roughly +/- 9 points of uncertainty.
- **Suggestive marks are weak (about 25%).** They mostly get called Descriptive (Coppertone, Chicken of the Sea, Orange Crush).
- **Famous brands get pulled toward Generic.** Microsoft, Netflix, Airbus and Amazon are misclassified in every run.
  All training marks are synthetic and invented, so the model has never seen a famous real brand.
- **Overconfident on real marks.** Probabilities were calibrated on a synthetic holdout (ECE 0.008) but are
  overconfident on LegalBench (ECE 0.145 for this seed, about 0.16 averaged over seeds). Treat the probabilities as a ranking, not as calibrated odds.
- **Sub-question labels are derived, not annotated.** The training data labels only the final category. Sub-question
  gold was derived from the routing rule plus fixed conventions (for example, a generic name "conveys" its goods).
- **Not legal advice.** This is a research demo, not a clearance or registrability opinion.

## Usage

Needs a GPU and Unsloth (trained with `unsloth==2026.10.3`). It loads with `FastDecisionModel`, **not** a
`transformers` text-generation pipeline.

```bash
pip install unsloth==2026.10.3 unsloth_zoo==2026.10.3
```

```python
from unsloth import FastDecisionModel

model, tokenizer = FastDecisionModel.from_pretrained("DoodDood/abercrombie-decision")
FastDecisionModel.for_inference(model)

CATEGORIES = ["Generic", "Descriptive", "Suggestive", "Arbitrary", "Fanciful"]
QUESTIONS = {
    "coined": {"type": "noul", "instructions": "Is the mark an invented term created solely for trademark use, with no prior independent meaning?"},
    "semantic_link": {"type": "noul", "instructions": "Does the mark's ordinary dictionary meaning have any plausible semantic relationship to the goods or services?"},
    "imagination": {"type": "noul", "instructions": "Must the consumer use imagination, thought, or a multi-step mental process to connect the mark to the nature of the goods or services?"},
    "conveys": {"type": "noul", "instructions": "Does the mark immediately convey an idea of a feature, quality, function, ingredient, or characteristic of the goods or services to the relevant purchasing public?"},
    "genus": {"type": "noul", "instructions": "Does the relevant purchasing public understand the mark primarily as the name of the general category of goods or services, rather than as an indicator of source?"},
    "distinctiveness": {
        "type": "score",
        "instructions": "Where does the mark fall on the Abercrombie spectrum of trademark distinctiveness for these goods or services?",
        "criteria": [
            "Generic: the common name of the goods or services; never protectable.",
            "Descriptive: directly describes a feature, quality or purpose of the goods.",
            "Suggestive: hints at the goods but needs imagination to connect.",
            "Arbitrary: a real word with no relationship to the goods.",
            "Fanciful: an invented word with no prior meaning.",
        ],
    },
}

# Input format: `The mark "X" for Y.` (the LegalBench phrasing it was trained on)
answers = FastDecisionModel.predict(model, tokenizer, 'The mark "Kodak" for cameras.', QUESTIONS)
final = answers["distinctiveness"]
print(CATEGORIES[final["answer"]], final["probabilities"])
for q in ["coined", "semantic_link", "imagination", "conveys", "genus"]:
    print(q, answers[q]["answer"], answers[q]["probabilities"])
```

**Use the question wording exactly as above.** The instruction and criteria text is part of the model's
input, so paraphrasing it shifts the predictions.

### Sub-questions and routing

The five sub-questions follow the GRPO model's doctrine decomposition:

- If coined = Yes, classify as Fanciful.
- Else if semantic_link = No, classify as Arbitrary.
- Else if genus = Yes, classify as Generic.
- Else if conveys = Yes, classify as Descriptive.
- Else if imagination = Yes, classify as Suggestive.

The head predicts the distinctiveness score directly. The routing rule is a consistency check you can run
on the sub-answers, not how the final answer is produced.

## Training

- **Method:** Unsloth `FastDecisionModel` with a Clef decision head, 16-bit LoRA (r = 16, alpha = 16) on
  `unsloth/Qwen3.5-4B`, 2 epochs, learning rate 2e-4, cosine schedule, batch size 8. The head was trained
  at Unsloth's default head learning rate.
- **Data:** the same 2,100 synthetic marks as Abercrombie-GRPO, balanced across the five classes and rephrased into
  LegalBench form. 1,890 rows were used for training. 210 held-out rows were used for temperature calibration.
- **Seeds:** 3407, 42 and 1234. Seed 42 is published because, among the six-question runs, it tied for
  best accuracy and had the highest routing agreement and the lowest LegalBench calibration error (ECE 0.145).
- **Compute:** about 7 minutes per run on one Colab G4 GPU.

## Acknowledgements

Huge thanks to **Daniel Han** and the **Unsloth** team for decision models in Unsloth. Going from a dataset to
a calibrated decision model in an afternoon on Colab is remarkable. The decision head follows the design
of Cloudflare's Clef.