Brello EI 0 - Emotional Intelligence AI Model
Created by Epic Systems | Engineered by Rehan Temkar
A locally-run emotional intelligence AI model designed to provide empathetic, emotionally-aware responses with natural conversation flow.
🚀 Features
- Emotional Intelligence: Designed to provide empathetic, understanding responses
- Local Operation: Runs completely locally without external dependencies
- Memory Efficient: 4-bit quantization for optimal performance on limited hardware
- Advanced Architecture: Based on Llama 3.2 3B foundation model
- Easy Integration: Simple API for quick integration
- Flexible Configuration: Customizable generation parameters
📦 Installation
Prerequisites
- Python 3.8+
- CUDA-compatible GPU (recommended) or CPU
- At least 8GB RAM (16GB recommended)
Install Dependencies
pip install -r requirements.txt
Model Options
Option 1: Use Public Model (Recommended for quick start)
The default configuration uses microsoft/DialoGPT-medium which is publicly available and doesn't require authentication.
Option 2: Use Llama 3.2 3B (Requires authentication) To use the actual Llama 3.2 3B model:
- Create a Hugging Face account
- Accept the model license at: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
- Login with:
huggingface-cli loginorhf auth login - Update the model_path in your code to:
"meta-llama/Llama-3.2-3B-Instruct"
Option 3: Use Other Public Models
microsoft/DialoGPT-large(larger, better responses)microsoft/DialoGPT-small(faster, smaller)HuggingFaceTB/SmolLM3-3B(3B parameter model)
🎯 Quick Start
Basic Usage
from brello_ei_0 import BrelloEI0
# Load the model
model = BrelloEI0(
model_path="microsoft/DialoGPT-medium", # Public model, no auth required
load_in_4bit=False # Set to True if you have CUDA
)
# Generate an emotionally intelligent response
response = model.generate_response("I'm feeling really stressed about my job interview.")
print(response)
Alternative Loading
from brello_ei_0 import load_brello_ei_0
# Load model using convenience function
model = load_brello_ei_0("microsoft/DialoGPT-medium")
# Direct call
response = model("I'm really happy about my recent success!")
print(response)
Chat Interface
# Simple chat
response = model.chat("How are you feeling today?")
print(response)
🎮 Example Conversations
# Example 1: Anxiety Support
response = model.generate_response("I'm feeling really anxious about my presentation tomorrow.")
# Output: "I can understand how nerve-wracking presentations can be. It's completely natural to feel anxious..."
# Example 2: Celebrating Success
response = model.generate_response("I just got promoted at work!")
# Output: "That's wonderful! I can feel your excitement and it's absolutely contagious..."
# Example 3: Emotional Support
response = model.generate_response("I'm feeling lonely and isolated.")
# Output: "I'm so sorry you're feeling this way. Loneliness can be really painful..."
# Example 4: Career Guidance
response = model.generate_response("I'm confused about what I want to do with my life.")
# Output: "That's a really common and natural feeling, especially when we're at crossroads..."
⚙️ Configuration
Model Parameters
model_path: Path to Llama 3.2 3B model (default: "meta-llama/Meta-Llama-3.2-3B-Instruct")device: Device to load model on ('cuda', 'cpu', etc.)load_in_4bit: Enable 4-bit quantization for memory efficiency (recommended)load_in_8bit: Enable 8-bit quantization for memory efficiencytorch_dtype: Torch data type for model weights
Generation Parameters
temperature: Sampling temperature (default: 0.7)top_p: Top-p sampling parameter (default: 0.9)max_length: Maximum response length (default: 4096)min_length: Minimum response length (default: 30)max_new_tokens: Maximum new tokens to generate (default: 256)repetition_penalty: Penalty for repetition (default: 1.1)
📊 Performance
Model Specifications
- Foundation: Microsoft DialoGPT-medium
- Parameters: 345 Million
- Context Length: 1024 tokens
- Training: Conversational dialogue data
- Optimization: Emotional intelligence focus
Memory Requirements
- Full Precision: ~1GB VRAM
- 8-bit Quantization: ~500MB VRAM
- 4-bit Quantization: ~250MB VRAM (recommended)
🔧 Advanced Usage
Custom Generation Parameters
response = model.generate_response(
"I'm feeling overwhelmed with my responsibilities.",
temperature=0.8,
top_p=0.95,
max_new_tokens=300,
repetition_penalty=1.05
)
Batch Processing
messages = [
"I'm really proud of my accomplishments.",
"I'm feeling uncertain about my future.",
"I'm grateful for my support system."
]
responses = []
for message in messages:
response = model.generate_response(message)
responses.append(response)
🎯 Training
Fine-tune for Emotional Intelligence
python train_brello_ei_0.py
The training script will:
- Load Llama 3.2 3B with 4-bit quantization
- Apply LoRA for efficient fine-tuning
- Train on emotional intelligence data
- Save the fine-tuned model
Training Data
The model is fine-tuned on emotional intelligence scenarios:
- Anxiety and stress support
- Celebrating success and achievements
- Dealing with loneliness and isolation
- Career guidance and life decisions
- Gratitude and appreciation
- Overwhelm and responsibility management
🏗️ Architecture
Brello EI 0 is built on advanced language model architecture with the following key components:
- Base Model: Microsoft DialoGPT-medium
- Tokenizer: Optimized for conversational data
- Generation: Emotionally intelligent response patterns
- Post-processing: Response cleaning and enhancement
- Quantization: 4-bit for memory efficiency (optional)
🎯 Use Cases
Emotional Support
- Providing empathetic responses to stress and anxiety
- Supporting users through difficult life transitions
- Celebrating achievements and successes
Personal Development
- Career guidance and decision-making support
- Life goal exploration and planning
- Self-reflection and emotional awareness
Mental Health Support
- Stress management and coping strategies
- Emotional validation and understanding
- Positive reinforcement and encouragement
🤝 Contributing
This model is part of the Epic Systems AI initiative. For questions or contributions, please contact the development team.
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
- Epic Systems for the vision and support
- Rehan Temkar for engineering and development
- Microsoft for the DialoGPT foundation model
- Hugging Face for the transformers library
Brello EI 0 - Bringing emotional intelligence to AI conversations 💙✨