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#!/usr/bin/env python3
"""
Model Options - Brello EI 0
Created by Epic Systems | Engineered by Rehan Temkar
Demonstrates different model options for Brello EI 0.
"""
from brello_ei_0 import BrelloEI0
def test_model_option(model_path, description):
"""Test a specific model option"""
print(f"\n🤖 Testing: {description}")
print(f"Model: {model_path}")
print("-" * 50)
try:
# Load the model
model = BrelloEI0(
model_path=model_path,
load_in_4bit=False
)
# Test emotional intelligence
test_message = "I'm feeling really stressed about my presentation tomorrow."
response = model.generate_response(test_message)
print(f"Input: {test_message}")
print(f"Response: {response}")
print("✅ Model working!")
return True
except Exception as e:
print(f"❌ Model failed: {e}")
return False
def main():
"""Test different model options"""
print("🤖 Brello EI 0 - Model Options")
print("Created by Epic Systems | Engineered by Rehan Temkar")
print("=" * 60)
# Available model options
model_options = [
{
"path": "microsoft/DialoGPT-medium",
"description": "Public Model (Recommended for quick start)"
},
{
"path": "microsoft/DialoGPT-large",
"description": "Larger Public Model (Better responses)"
},
{
"path": "microsoft/DialoGPT-small",
"description": "Smaller Public Model (Faster)"
}
]
# Test each model option
working_models = []
for option in model_options:
if test_model_option(option["path"], option["description"]):
working_models.append(option)
print(f"\n📊 Results:")
print(f"✅ Working models: {len(working_models)}/{len(model_options)}")
if working_models:
print("\n🎯 Recommended models:")
for model in working_models:
print(f" • {model['path']} - {model['description']}")
print("\n💡 To use Llama 3.2 3B:")
print("1. Create Hugging Face account")
print("2. Accept license at: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct")
print("3. Login with: huggingface-cli login")
print("4. Update model_path to: 'meta-llama/Llama-3.2-3B-Instruct'")
if __name__ == "__main__":
main()
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