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memnar-system1

ExecuTorch
English
on-device
memory
wearables
information-extraction
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metadata 
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.

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.