| --- |
| license: other |
| license_name: fair-noncommercial-research-license |
| license_link: https://huggingface.co/facebook/MobileLLM-R1-360M-base/blob/main/LICENSE |
| base_model: facebook/MobileLLM-R1-360M-base |
| language: |
| - en |
| tags: |
| - on-device |
| - executorch |
| - memory |
| - wearables |
| - information-extraction |
| --- |
| |
| # Remembery System 1 (v4): on-device memory for AI glasses |
|
|
| Two fine-tuned **MobileLLM-R1-360M** models with non-autoregressive heads that turn a live conversation transcript into |
| memories on the phone. Each window (~2,000 characters of transcript) gets one forward pass per model; the pooled graphs |
| (entities, frame triggers, roles, attributes) pick candidate lines, and a small logistic validator keeps the ones |
| worth remembering as structured records. |
|
|
| Code, app and instructions: [github.com/IAMIbrahimmemon/remembery](https://github.com/IAMIbrahimmemon/remembery). |
|
|
| ## Scores |
|
|
| Held-out real conversations (Switchboard, Taskmaster, meetings); fact checklists and judging by Muse Spark. |
|
|
| | System | Recall | Precision | F1 | |
| |---|---|---|---| |
| | This system, sealed set (85 conversations, scored once) | 0.799 | 0.783 | **0.791** | |
| | This system, dev set (89 conversations) | 0.774 | 0.788 | 0.781 | |
| | Large LLM labeling the same dev conversations| 0.814 | 0.892 | 0.851 | |
|
|
| Wrong memories 0.2%. On an iPhone 17: 4.5–5.4 s per full window for both models, ~4.5 GB footprint. |
|
|
| ## Files |
|
|
| | Path | What | |
| |---|---| |
| | `checkpoints/v4e/best.pt` | model A (PyTorch state dict, fp32) + `args.json`, `labels3.json` | |
| | `checkpoints/v4f/last.pt` | model B, same layout | |
| | `validator_g2.json` | line validator (logistic weights, cutoff 0.60, feature spec) | |
| | `vocab3.json` | pruned vocabulary used for the phone export | |
| | `ios/memnar-ios-model-v4.zip` | `ios/Model/`: model A as an ExecuTorch `.pte` (XNNPACK, fp32), tokenizer, metadata, parity fixtures | |
| | `ios/memnar-ios-model-v4-b.zip` | `ios/Model_b/`: model B, same layout | |
| | `ios/memnar-ios-phonon.zip` | `ios/Phonon/`: Phonon-2.5 English speech to text converted to Core ML (weights unchanged) | |
|
|
| Both models are needed: the score above is for the two pooled, run with cutoffs entity 0.5, trigger 0.5, role 0.35 and |
| the validator at 0.60. |
|
|
| sha256: |
|
|
| ``` |
| a020c56919872a17eae942ef934031a1c2125525770b752f3e58af0d31e0900b checkpoints/v4e/best.pt |
| b484ad260cd4b87a4524a6a26be1e7491db06a76b0ca49d6deccdafab3786415 checkpoints/v4f/last.pt |
| 9601602dc6f864d59ea2f2d8e7b4442947cd6d06cb3c1ecae8e7238924778196 ios/memnar-ios-model-v4.zip |
| 7b18bbabfab1e251388534150b570b0fbe8870bb66d5d953aeb8d9614cb725f0 ios/memnar-ios-model-v4-b.zip |
| 4dfc5b3b24ca12e7351bc5f6a3540b26d3d6439633954b834c91f751bb1f87aa ios/memnar-ios-phonon.zip |
| ``` |
|
|
| ## Use |
|
|
| ``` |
| git clone https://github.com/IAMIbrahimmemon/remembery && cd remembery && uv sync |
| hf download IAMIbrahim/memnar-system1 --include "checkpoints/*" --local-dir hf |
| PYTHONPATH=. uv run python memories.py --ckpts hf/checkpoints --windows data/probe/probe_v1.jsonl --out memories.jsonl |
| ``` |
|
|
| ## Licenses |
|
|
| - System 1 checkpoints and `.pte` files: derivatives of MobileLLM-R1-360M-base, under the FAIR Noncommercial Research |
| License (noncommercial research use only). |
| - `ios/memnar-ios-phonon.zip`: Phonon-2.5 English by FermionResearch (a compressed nvidia/parakeet-tdt-0.6b-v3), |
| CC-BY-4.0, converted to Core ML without changing the weights. |
|
|
| Built for the Meta Global AI Developer Hackathon 2026. |
|
|
