Starlight Prod

--- license: apache-2.0 tags: - steganography - steganalysis - gguf - balanced-detector - image-classification - computer-vision - bitcoin library_name: gguf pipeline_tag: image-classification datasets: - custom metrics: - accuracy - f1 - auc --- # Model Card: Starlight Balanced Detector (GGUF) ## Model Overview - **Task**: Steganography detection (and method / bit-order heads) - **Architecture**: `BalancedStarlightDetector` (multi-stream CNN + fusion gate) - **Input streams**: pixel, meta, alpha, LSB, palette, format/content features (see GGUF export docs) - **Primary artifact**: **GGUF** for Stargate / Trin (Go) ## Artifacts (this repo on HF) | File | Description | |------|-------------| | `starlight.gguf` | **Primary** production weights (GGUF v3 / F32) | | `starlight_gguf_map.json` | Tensor name map and export metadata | | `README.md` | This model card | Optional secondary (may appear if uploaded): `detector_balanced.pth`, `detector_balanced.onnx`. ## Inference **Production**: load `starlight.gguf` via **Stargate / Trin** GGUF path — not Python ONNX as the primary path. Download: ``` https://huggingface.co/macroadster/starlight-prod/resolve/main/starlight.gguf https://huggingface.co/macroadster/starlight-prod/resolve/main/starlight_gguf_map.json ``` ## Training - **Dataset**: Combined submissions (grok, gemini, claude, chatgpt, sample, val) - **Checkpoint**: `models/detector_balanced.pth` (training repo) - **Export**: `scripts/export_starlight_gguf.py` → `models/starlight.gguf` - **Typical recipe**: Adam, balanced clean/stego sampling (see training repo `trainer.py`) ## Steganography coverage - `lsb`, `alpha`, `exif`, `eoi` / raw, `palette` (and related variants in datasets) ## Performance | Metric | Value | |--------|-------| | Accuracy | see training run / leaderboard | | False positive focus | balanced detector design | Update metrics after each publish-worthy training run. ## License - Model: Apache 2.0 - Code: see training repository LICENSE