O1-Flash

A liquid-core intent router — 4.53M parameters answering 77-way customer-service routing as a calibrated multiple-choice decision.

Part of the AwareLiquid O-Series. The recurrent core carries per-channel learnable time constants (τ, geometrically spaced), so one small model holds both phoneme-scale and utterance-scale state with a fixed-size hidden state.

Honest status

Measured on banking77 (10,003 train / 3,080 test rows, 77 intents, byte-level tokenizer, no external vocab), default config d256/4L/4.53M params, batch 64:

steps	test acc	ECE	ECE (temperature-scaled)
1,500	23.28%	—	—
20,000 (lr 1e-3)	30.23%	0.485	0.161
20,000 (scheduled 3e-3)	33.28%	0.469	0.161
200,000 (scheduled)	40.75%	0.456	0.127

• Chance = 1.3%. The default-config model is far from a production router; 40.75% at 4.53M params is the honest ceiling measured so far.
• Calibration works: temperature scaling (LBFGS on a held-out half) cuts ECE by ~3.6× on the final model (T=4.41).
• Training-stability finding (2026-10-04): at lr 3e-3, training diverges under GPU contention (learns, then collapses to chance — reproduced on two machines). Fix: --lr 1e-3 default plus linear-warmup + cosine schedule. The 200k run above ran fully stable under the schedule.
• The temperature parameter (T=4.41) is saved with the evaluation artifact, not baked into the checkpoint: apply it to the softmax only when calibrated probabilities are needed.

Files

• o1flash_banking77_200k_sched.pt — PyTorch checkpoint (18.2 MB, 4.53M params, trained 200k steps with the lr schedule). Contains model state dict + config.

Usage

import torch
from benchmarks.real_data_bench import O1Flash, FlashConfig  # github.com/AwareLiquid/O1-Flash

ck = torch.load("o1flash_banking77_200k_sched.pt", map_location="cpu",
                weights_only=True)
model = O1Flash(FlashConfig(**ck["config"]))
model.load_state_dict(ck["model"])

Limits

• English customer-service text only; byte-level tokenizer.
• 40.75% top-1 on 77 intents — not production. The point of this checkpoint is reproducibility of the measured numbers and the stability/calibration findings, not deployment.
• The same architecture/discussion as the rest of the O-Series: see EverestAn/MT-LNN for the family card and the awareliquid.ai benchmark page.