| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - conversational |
| - emotional-intelligence |
| - brello-ei |
| - epic-systems |
| - ai-assistant |
| - empathy |
| - mental-health |
| - support |
| pipeline_tag: text-generation |
| --- |
| |
| # 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 |
|
|
| ```bash |
| 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: |
| 1. Create a Hugging Face account |
| 2. Accept the model license at: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct |
| 3. Login with: `huggingface-cli login` or `hf auth login` |
| 4. 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 |
| |
| ```python |
| 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 |
| |
| ```python |
| 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 |
| |
| ```python |
| # Simple chat |
| response = model.chat("How are you feeling today?") |
| print(response) |
| ``` |
| |
| ## Example Conversations |
| |
| ```python |
| # 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 efficiency |
| - `torch_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 |
|
|
| ```python |
| 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 |
|
|
| ```python |
| 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 |
|
|
| ```bash |
| 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 💙✨ |
|
|
