Spaces:
Running
Running
| title: SAM-MM Multimodal Demo | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 6.18.0 | |
| app_file: app.py | |
| pinned: false | |
| license: other | |
| short_description: 58M offline multimodal model for vision and physics | |
| # SAM-MM — multimodal reasoning demo | |
| Interactive demo of **SAM-MM** (AMFORGE), a ~58M-parameter offline multimodal model that | |
| perceives synthetic frames (and a log-mel spectrogram for audio scenes) and answers with | |
| either a `[CHAT]` reasoning trace or a `[ACTION]` JSON record. | |
| Pick a scene; each one is **freshly generated** and the model decodes token-by-token — | |
| nothing is hard-coded. Physics and motion are the model's strength (its latent world-model | |
| carries real dynamics); OCR generalizes to unseen numbers; the cross-modal action family | |
| is weaker; **audio is the weak modality** (trained on synthetic pseudo-mel). | |
| ## Setup | |
| The model weights live in **private** HuggingFace repos. Add a Space **secret** named | |
| `HF_TOKEN` (a token with read access to the `AMFORGE/sam-mm-*` repos). The app pulls the | |
| checkpoint and tokenizer from the Hub on first run. | |
| Architecture internals, tokenizer construction, and data generators are proprietary and | |
| not exposed by this demo. |