Property	Value
Base Model	Qwen2.5-1.5B-4bit (via mlx-community)
Fine-tune Method	LoRA (rank 8) fused into base weights
Format	MLX SafeTensors (Apple Silicon optimized)
Training Hardware	Mac Studio M2 Ultra (M5 node)
Author	Ioan George Anton (Architect)
Purpose	Fast intent classification before expensive C0 cortex fires

Architecture Role

This model is the C1 Classifier — the second layer in SIFTA's five-layer decision pipeline:

Reflex Arc → instant safety responses
C1 Classifier (THIS MODEL) → fast intent detection (~1.5B, sub-second)
Basal Ganglia → action selection
Corpus Callosum → cross-modal integration
C0 Cortex → full reasoning (alice-cortex-v1)

Why two models? The C1 classifier handles ~80% of incoming intents at 1/3 the compute cost. The expensive C0 cortex only fires when the classifier can't resolve the intent. This is biological: your brainstem handles reflexes before your prefrontal cortex even wakes up.

Usage (MLX)

python

from mlx_lm import load, generate

model, tokenizer = load("georgeanton/alice-classifier-v2")
response = generate(model, tokenizer, prompt="Classify intent: play some music", max_tokens=32)
print(response)

Part of SIFTA

588 system modules | 17 biological organs | 4 provisional patents | 2,532+ commits

Repository: github.com/antonpictures/ANTON-SIFTA

License

Apache 2.0 — For the Swarm. 🐜⚡