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AwareLiquid/
O1-48M

Name: O1-48M
Availability: InStock

liquid-neural-networks

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MT-LNN — Microtubule-Inspired Liquid Neural Network (O1-48M)

▶️ Try it in HyperCode — the production app with the same liquid-core brain: 228+ built-in domain skills, on-device, one-click install for Windows & macOS.

Honest status first: research artifact, not a general assistant. This is the O1-48M attention-free edge model trained from scratch — the O-Series line of the AwareLiquid research project. It does not aim to match frontier models on dialogue or benchmarks; its claims are about memory form-factor and efficiency, and every number below is measured and reproducible.

What it is

48M params, attention-free, from scratch — no base model
O(1) constant inference state: 0.381 MB flat state from 512 to 1,048,576 tokens (vs a KV cache growing to 3,072 MB — 8,063× smaller)
Continuous-time liquid core with learnable time constants (13 protofilaments × 5 time-scales), true parallel-scan recurrence
CPU-friendly: the live demo at awareliquid.ai/demo runs on CPU

Measured results (multi-seed, reproducible)

Result	Number
Cross-window associative recall (fast-weight memory)	0.56 (3 seeds) vs 0.000 for attention/LoRA
O(1) inference state @1M tokens	0.381 MB constant, 8,063× smaller than KV
Robustness to irregular sampling (NASA battery SoH)	+7.7% degradation @80% dropped samples (LSTM +31.1%, GRU +32.8%)
Language-modeling quality	Not an advantage: 125M-scale WikiText-103 PPL 88.93 ± 0.33 vs Transformer 78.86 ± 0.25

The architecture's case rests on memory form-factor and efficiency, not on quality per parameter. Full honest analysis: RESULTS.md.

Weights

This checkpoint is published on GitHub Releases: o1-48m-v1.

Serve it:

CKPT_PATH=o1_48m_serve.pt TOKENIZER=gpt2 python -m uvicorn serve.server:app

Paper

Code: AwareLiquid/M1 (MIT)
Live demo: awareliquid.ai/demo
Product: HyperCode — same brain, 228+ skills, on-device
Model family: awareliquid.ai

AwareLiquid/O1-48M