Strip model card to minimal -- full docs live in the GitHub repo

README.md

@@ -3,90 +3,22 @@ license: other
 tags:
   - image-classification
   - ai-generated-image-detection
-  - lorc
   - dinov3
 pipeline_tag: image-classification
 ---
 
-# mLoRC (Modulated-LoRC)
+# mLoRC
 
-An [LoRC](https://arxiv.org/abs/2608.20882) (Low-Rank Collapse) AI-generated-image
-detector: a frozen **DINOv3 ViT-H+/16** backbone + LoRA adapters, an orthogonal
-decomposition of the patch tokens against the [CLS] token, and a Low-Rank
-Attention Block on the residual subspace, fine-tuned on the full
-[DDA-Training-Set](https://arxiv.org/abs/2608.20882) (118,287 real/fake
-pairs) with **pair-aware energy augmentation** — a training-time trick that
-randomly rescales each pair's residual-subspace magnitude (simulating
-different image compositions/energy bands) while mathematically guaranteeing
-the real>fake energy ordering *within* every pair is preserved exactly.
-
-`attn_rank=64`, LoRA `rank=32/α=32` (folded into the backbone weights below,
-not shipped as a separate adapter).
-
-## Why pair-aware, not per-sample
-
-An earlier version of this augmentation drew an independent random scale for
-every image, real and fake alike. That let a real image get scaled down
-while its own paired fake got scaled up in the same batch — a real,
-quantified risk (23.75% instantaneous real/fake energy-inversion rate per
-augmented draw, vs. a 3.80% natural baseline). This checkpoint's training
-draws **one shared scale factor per real/fake pair** instead — proven, not
-just observed, to leave the inversion rate exactly at the 3.80% baseline,
-since scaling both sides of a ratio by the same factor can't flip its sign.
-
-## Results (full 30,000-image WildFake eval)
-
-| | Clean BAcc | Clean AUC | Full (transformed) BAcc | Full AUC |
-|---|---|---|---|---|
-| v2 baseline (no aug) | 95.01% | 0.9911 | 91.92% | 0.9739 |
-| **mLoRC** | **96.57%** | **0.9929** | **92.65%** | 0.9723 |
-
-Biggest gains: real-photo groups that were previously the model's weakest
-point — celebahq (85.4%→94.7% under transforms), ffhq (84.3%→94.4%). Full
-per-generator breakdown, throughput benchmarks, and training details: see
-[Buxt-Codes/AIGI-mLoRC](https://github.com/Buxt-Codes/AIGI-mLoRC) — the code
-that loads this checkpoint lives there, not in this HF repo.
-
-Known regression: Imagen (Google) under transforms, 91.9%→90.9% — the one
-generator where this trick's real-photo gains don't fully offset a drop in
-raw recall on that specific generator (93.7%→86.1%).
-
-## Usage
-
-Clone [Buxt-Codes/AIGI-mLoRC](https://github.com/Buxt-Codes/AIGI-mLoRC), set
-up its `requirements.txt`, then:
+AI-generated-image detector (DINOv3 ViT-H+/16 + [LoRC](https://arxiv.org/abs/2608.20882)).
+Code, results, and docs: [Buxt-Codes/AIGI-mLoRC](https://github.com/Buxt-Codes/AIGI-mLoRC).
 
 ```python
 from mlorc import ModulatedLoRC
-
-model = ModulatedLoRC.from_pretrained()      # pulls mlorc-full.pt from this repo
-result = model.predict_image("photo.jpg")
-print(result)   # {"label": "fake", "p_fake": 0.93, "p_real": 0.07}
+model = ModulatedLoRC.from_pretrained(hf_token="hf_...")
+model.predict_image("photo.jpg")
 ```
 
-or, for a whole directory of images:
-
-```bash
-python predict.py --input_dir <path/to/images> --output results.json
-```
-
-This repo is **private** — pass a token (`from_pretrained(hf_token="hf_...")`
-or set `HF_TOKEN`/run `huggingface-cli login`) to access it. Getting access
-here is the *only* gate: unlike the base DINOv3 checkpoint
-(`facebook/dinov3-vith16plus-pretrain-lvd1689m`, gated with **manual review**
-by Meta), loading this model never touches that repo at all — see `Files`.
-
 ## Files
 
-- `mlorc-full.pt` (~1.6GB, bf16) — **one self-contained checkpoint**: the
-  DINOv3 ViT-H+/16 backbone with the trained LoRA adapters already folded
-  into its weights (`peft`'s `merge_and_unload()`, verified numerically
-  exact before shipping — max output difference vs. the pre-merge model was
-  1e-12, pure floating-point noise), plus the Low-Rank Attention Block and
-  classifier head.
-- `dinov3_config.json` — DINOv3's architecture metadata (hidden size, layer
-  count, etc.) mirrored here, **not its weights**. Together with
-  `mlorc-full.pt` this means loading the model never needs access to
-  `facebook/dinov3-vith16plus-pretrain-lvd1689m` at all — verified with that
-  repo's local cache deliberately removed before loading, output still
-  bit-identical. No adapter file, no second HF repo, no Meta gate to clear.
+- `mlorc-full.pt` — full checkpoint, LoRA merged into the backbone weights.
+- `dinov3_config.json` — DINOv3 architecture config (no weights).