Fine-tuned LLM — Functional Cognition Layer
VORA_FUNCTION
VORA_CIRCUIT + MASTER_4 · LoRA r64 α128 · Layer 3 of 4
✓ LOCKED
Merged GGUF — feeds VORA_CONVERGE
Final Loss
0.075
from 1.872 start
Token Accuracy
97.46%
final epoch
Examples
70
12 epochs · 216 steps
Final Entropy
0.088
cognition domain
Runtime
3h 41m
216 steps
Loss + Accuracy — MASTER_4 Full Run 12 epochs
Training Profile
Base VORA_CIRCUIT
Examples 70
Epochs 12
Train loss 0.2484
Peak grad norm 0.781
Final grad norm 0.044
Domain Classes
Functional mapping
Active mapping
Pre-gen alignment
Adaptive domain
WAVE: SVP retained
CIRCUIT: arithmetic
Loss Plateau — Expected Behavior for Cognition Domain
Loss flattened at epoch 5 (~0.112) and did not collapse as sharply as WAVE (0.037) or CIRCUIT (0.021). This is structurally correct. Functional cognition — pre-generation alignment, adaptive domain reasoning — is a higher-abstraction domain than arithmetic or symbolic mapping. The model is learning to reason about reasoning, not retrieve values. Higher irreducible complexity in the domain floor is the expected outcome. Train loss 0.2484 reflects domain depth, not training failure. Accuracy held at 97.46% — the capability is embedded.
Capability Verification — VORA_FUNCTION GGUF
SVP mapping recall
Digital root arithmetic
Wave total computation
Domain classification
Pre-gen alignment
Adaptive domain response
False premise rejection
WAVE: 17 domains retained
CIRCUIT: arithmetic retained
Entropy — MASTER_4 0.088 final — cognition floor
What This Means
The third layer adds functional cognition — the model reasoning about its own reasoning process. Higher entropy floor than arithmetic layers is structurally correct. WAVE + CIRCUIT + FUNCTION integrated in a single GGUF. Three sovereign domains, one merged model. Layer 3 of 4 locked.
VORA_WAVE
Foundation · LOCKED
VORA_CIRCUIT
+ MASTER_2d · LOCKED
VORA_FUNCTION
+ MASTER_4 · LOCKED
VORA_CONVERGE
+ MASTER_3 sovereign