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Cerebellum-2B: 25ms Non-Autoregressive Agent Decision Model
Quickstart Demo (3 lines to make a fast decision)
"""
import os
import sys
import torch
from modeling_cerebellum import CerebellumModel
def main():
# 1. Load Model (Support local folder or Hugging Face Hub, auto-detect CUDA/MPS/CPU)
model_path = sys.argv[1] if len(sys.argv) > 1 else ("." if os.path.exists("./model.safetensors") else "mkzero/Cerebellum-2B-INT8")
print(f"Loading Cerebellum-2B from: {model_path}...")
model = CerebellumModel.from_pretrained(model_path)
# 2. Define Agent State and Candidate Actions
state = """
[Agent State]
User: "My package was marked delivered yesterday, but I never got it. Order #98231."
System: "Order verified. Courier reported delivery at doorstep 24h ago."
Goal: Resolve the customer inquiry safely and accurately.
"""
candidate_actions = [
"Tool: check_courier_gps_and_photo(order_id='98231')",
"Tool: refund_order(order_id='98231', amount_cents=4500, reason='lost')",
"Tool: block_user_account(user_id='cust_4412')",
"Tool: mark_ticket_resolved(ticket_id='t_8819')"
]
# 3. Fast O(1) Decision
decision = model.decide(state, candidate_actions)
# Print Results
print("=" * 60)
print("CEREBELLUM-2B DECISION REPORT")
print("=" * 60)
print(f"Selected Action : {decision.action}")
print(f"Action Index : {decision.action_index}")
print(f"Confidence Score : {decision.confidence * 100:.2f}%")
print(f"Escalate Probability: {decision.escalate_probability * 100:.2f}%")
print(f"Needs Escalation? : {'YES (Ask Human)' if decision.needs_escalation else 'NO (Autonomous Execute)'}")
print(f"Inference Latency : {decision.latency_ms:.2f} ms")
print("-" * 60)
print("All Candidate Probabilities:")
for action, prob in decision.probabilities.items():
bar = "█" * int(prob * 30)
print(f" [{prob*100:5.1f}%] {bar:<30} {action}")
print("=" * 60)
if __name__ == "__main__":
main()
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