VERTAG (ACCV 2026): release vertag-explainer-lora

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LICENSE.txt

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+VERTAG model weights (ACCV 2026)
+Copyright (c) 2026 VERTAG authors
+
+The model files in this folder
+
+  FADE/fade_dinov2_vitl14_reg.safetensors
+  FADE/fade_siglip_so400m.safetensors
+  explainer/explainer_lora/
+  explainer/explainer_lora_prompt_v0/
+
+are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0):
+https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+You may use, share and adapt them for non-commercial purposes, with attribution. Commercial use is not
+permitted. If you use them, please cite the VERTAG paper (see https://github.com/spaces-lalala/VERTAG).
+
+The weights are derived from the following models, which remain under their own licenses
+(Apache License 2.0, full text in LICENSE-APACHE-2.0.txt):
+
+  - DINOv2 ViT-L/14 with registers (https://github.com/facebookresearch/dinov2)
+  - SigLIP SO400M/14, 224 px (https://huggingface.co/google/siglip-so400m-patch14-224)
+  - Qwen2.5-VL-7B-Instruct (https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
+
+The FADE files contain only the layers that training changed (the last two transformer blocks, the final
+norm and the FADE head); the unchanged backbone weights are downloaded from their official sources when the
+code loads a model. The explainer folders are LoRA adapters for Qwen2.5-VL-7B-Instruct.
+
+The generated explanations are research outputs, not decisions or opinions of the Taiwan Intellectual
+Property Office and not legal advice.

README.md

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+---
+license: cc-by-nc-4.0
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+library_name: peft
+pipeline_tag: image-text-to-text
+language:
+- zh
+tags:
+- lora
+- peft
+- trademark
+- legal
+- vertag
+---
+
+# explainer LoRA (VERTAG, ACCV 2026)
+
+A LoRA adapter for `Qwen/Qwen2.5-VL-7B-Instruct`: given an applied trademark and the prior mark it was refused
+over, it writes an examiner-style rationale (Traditional Chinese) for why the two marks are similar, item by item
+over the visual likelihood-of-confusion factors. It is the grounded-explanation module of
+[VERTAG](https://github.com/spaces-lalala/VERTAG) (ACCV 2026). The code, the exact prompt and both adapters are
+described in [`explainer/`](https://github.com/spaces-lalala/VERTAG/tree/main/explainer).
+
+The **default** adapter. It was trained with the visual-only prompt that `generate.py` runs, and it is the
+adapter of the paper's registration-number grounding (Tab. 5, registration-number row: fabricated registration
+numbers fall from 100% to 5% at no cost in content).
+
+## Usage
+
+```bash
+git clone https://github.com/spaces-lalala/VERTAG.git
+cd VERTAG/explainer
+pip install -r requirements.txt     # GPU with ~17 GB free (bf16)
+python generate.py --applied applied.jpg --cited cited.jpg --regno 00820591 \
+    --condition C --regno-evidence --adapter lawundry/vertag-explainer-lora
+```
+
+`--adapter` takes this Hugging Face id directly; omit it to run the base model zero-shot.
+
+## Model card
+
+- **Source.** The adapter files used in the paper, not retrained ones.
+- **Architecture.** LoRA (r = 16, α = 32) on the q/k/v/o projections of the language model of
+  `Qwen/Qwen2.5-VL-7B-Instruct`; the vision tower is unchanged. Applied to the base model in bf16.
+- **Training data.** 6,000 applied → cited pairs from TIPO office actions published up to 2023, with the
+  examiner's similarity paragraph as the target; QLoRA (4-bit), 2 epochs. The paper's evaluation cases are
+  office actions after 2023.
+- **Limitations.** Without the cited registration number (`--regno-evidence`), a fine-tuned adapter almost
+  always writes a fabricated one (paper Tab. 5: 100%). The output is automatically generated research text,
+  not an examination opinion of TIPO and not legal advice.
+- **Prompt.** Trained with the prompt it is run with.
+
+| File | SHA256 |
+|---|---|
+| `adapter_model.safetensors` | `43c8d681f3f822303c885c5b8080a2a54098f5b8d8868ea518d1b6c969ea6597` |
+
+## License
+
+The adapter is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/); commercial use
+is prohibited. It is applied to `Qwen/Qwen2.5-VL-7B-Instruct` (Apache-2.0, `LICENSE-APACHE-2.0.txt`), whose
+terms also apply.
+
+## Citation
+
+```bibtex
+@inproceedings{yen2026vertag,
+  title     = {{VERTAG}: Visual Examiner Rationales for Trademarks with Atomic Grounding --- A Confusion Benchmark, Faithful Retriever, and Explanation-Coverage Metric},
+  author    = {Yen, Sheng-Yuan and Chou, Chia-Yi and Peng, Chi-Tse and Ye, Chian-Yu and Yu, Tsan-Wei and Ko, Chih-Chun and Wu, Yi-Chieh},
+  booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
+  year      = {2026}
+}
+```

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2_5_VLForConditionalGeneration",
+    "parent_library": "transformers.models.qwen2_5_vl.modeling_qwen2_5_vl"
+  },
+  "base_model_name_or_path": "Qwen/Qwen2.5-VL-7B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "k_proj",
+    "q_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:43c8d681f3f822303c885c5b8080a2a54098f5b8d8868ea518d1b6c969ea6597
+size 40403560