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Example usage of the Confessional Agentic Layer (CAL)
This script demonstrates how to:
1. Initialize the CAL model
2. Generate text with ethical oversight
3. Access the model's reasoning process
"""
import torch
from transformers import AutoTokenizer
from cal import CAL, CALConfig
def main():
# Initialize model configuration
config = CALConfig(
d_model=512,
nhead=8,
num_layers=6,
vocab_size=50000,
max_seq_length=1024,
device="cuda" if torch.cuda.is_available() else "cpu"
)
# Initialize model
print("Initializing CAL model...")
model = CAL(config)
# Load tokenizer (using GPT-2 as an example)
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
# Example prompts
prompts = [
"Explain the ethical implications of artificial intelligence",
"What are the potential risks of advanced AI systems?",
"How can we ensure AI systems remain beneficial to humanity?"
]
for prompt in prompts:
print(f"\n{'='*80}")
print(f"PROMPT: {prompt}")
print("-" * 80)
# Tokenize input
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
# Generate response
with torch.no_grad():
output = model(
input_ids,
max_length=150,
temperature=0.7
)
# Decode and print response
response = tokenizer.decode(output['output_ids'][0], skip_special_tokens=True)
print(f"RESPONSE: {response}")
# Print reasoning steps
print("\nREASONING STEPS:")
for i, step in enumerate(output['metadata']['scratchpad_steps'], 1):
print(f"{i}. {step['thought']}")
print(f" {step['result']}")
print("\nExample complete!")
if __name__ == "__main__":
main()
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