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config.py

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+from dataclasses import dataclass
+
+
+@dataclass(frozen=True, slots=True)
+class Profile:
+    # Transformer depth; shallow stacks limit overfitting on modest datasets
+    # and keep inference latency low on edge hardware.
+    num_layers: int
+    # Hidden width sized to match domain-string complexity without paying for
+    # unnecessary parameters.
+    d_model: int
+    # Multi-head attention count balances representational power against
+    # compute footprint for GPU inference.
+    nhead: int
+    # Feed-forward expansion keeps the standard 4x ratio for strong accuracy
+    # while maintaining throughput.
+    ffn_mult: int = 4
+    # Dropout of 10% reduces co-adaptation; higher rates slowed convergence in
+    # experiments.
+    dropout: float = 0.1
+    # AdamW learning rate chosen from sweep; 3e-4 converged stably without
+    # schedule tuning.
+    lr: float = 3e-4
+    # Weight decay curbs weight drift and improves generalization when
+    # training on long-running streams.
+    weight_decay: float = 0.01
+    # Label smoothing discourages over-confident logits and improves
+    # calibration on noisy threat labels.
+    label_smoothing: float = 0.05
+    # Sequence length covers observed second-level domains with spare room for
+    # rare longer names.
+    max_len: int = 64
+
+
+PROFILES = {
+    # ~3.2M params - for quick testing
+    "tiny": Profile(num_layers=4, d_model=256, nhead=8),
+    # ~10.8M params - balanced performance
+    "small": Profile(num_layers=6, d_model=384, nhead=6),
+}