README.md

metadata

title: AI Executive System
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.5.0
python_version: '3.11'
app_file: app/app.py
pinned: false

AI Executive System

A production-ready AI chatbot system that replicates the communication style, reasoning patterns, and personality of Ryouken Okuni, CEO of Akatsuki AI Technologies. The system uses a dual-LLM architecture with open-source models, designed for white-label deployment across multiple enterprise clients.

Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                           AI EXECUTIVE SYSTEM                                │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   [User Query]                                                               │
│        │                                                                     │
│        ▼                                                                     │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │  LLM 1: VOICE MODEL (Llama-3.1-8B-Instruct + LoRA)                    │   │
│   │  - Fine-tuned on CEO's blog content                                  │   │
│   │  - Captures authentic reasoning patterns & communication style       │   │
│   │  - Generates CEO-style draft response                                │   │
│   └─────────────────────────────────────────────────────────────────────┘   │
│        │                                                                     │
│        ▼                                                                     │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │  LLM 2: REFINEMENT MODEL (Llama-3.1-8B-Instruct)                    │   │
│   │  - No fine-tuning required (prompt-based)                            │   │
│   │  - Polishes grammar, clarity, professional formatting                │   │
│   │  - Improves logical flow and argument coherence                      │   │
│   │  - Ensures cultural appropriateness for Japanese business context    │   │
│   │  - Preserves voice authenticity while improving quality              │   │
│   └─────────────────────────────────────────────────────────────────────┘   │
│        │                                                                     │
│        ▼                                                                     │
│   [Final Response to User]                                                   │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Features

• Dual-LLM Pipeline: Voice model for authentic CEO communication + Refinement model for polish
• QLoRA Fine-tuning: Efficient training with 4-bit quantization
• Japanese Business Culture Awareness: Culturally appropriate responses
• Hugging Face Integration: Models stored and loaded from HF Hub
• Gradio Interface: Professional chat UI with custom branding
• Comprehensive Evaluation: Voice authenticity and factual accuracy metrics

Project Structure

ai-executive/
├── data/
│   ├── raw/                  # Original blog posts (blogs.txt)
│   ├── processed/            # Cleaned and segmented content
│   └── training/             # Final JSONL training datasets
│
├── src/
│   ├── data_processing/      # Blog parsing, Q&A generation
│   ├── training/             # QLoRA/LoRA fine-tuning scripts
│   ├── inference/            # Dual-LLM pipeline
│   └── evaluation/           # Voice and accuracy metrics
│
├── app/                      # Gradio application
├── scripts/                  # CLI tools
└── notebooks/                # Jupyter notebooks for experiments

Quick Start

1. Installation

pip install -r requirements.txt

2. Prepare Data

Place your CEO's blog content in data/raw/blogs.txt with the following format:

=== BLOG START ===
[Title of first blog post]

[Content of first blog post...]

=== BLOG END ===

=== BLOG START ===
[Title of second blog post]

[Content of second blog post...]

=== BLOG END ===

3. Process Blogs

python scripts/process_blogs.py --input data/raw/blogs.txt --output data/processed/

4. Generate Training Data

# Set your API key
export ANTHROPIC_API_KEY=your_key_here
# or
export OPENAI_API_KEY=your_key_here

# Generate Q&A pairs
python scripts/generate_training_data.py \
    --input data/processed/ \
    --output data/training/ \
    --num-pairs 500

5. Fine-tune the Model

# Run on Hugging Face infrastructure
python scripts/train_model.py \
    --dataset data/training/train.jsonl \
    --base-model Qwen/Qwen3-4B-Instruct \
    --output-repo your-username/ceo-voice-model \
    --epochs 3

6. Run the Chatbot

python app/app.py

Or deploy to Hugging Face Spaces:

python scripts/push_to_hub.py --space your-username/ai-executive-chatbot

Configuration

Environment Variables

Create a .env file in the project root:

# API Keys for Q&A generation
ANTHROPIC_API_KEY=your_anthropic_key
OPENAI_API_KEY=your_openai_key

# Hugging Face
HF_TOKEN=your_huggingface_token
HF_USERNAME=your_username

# Model Configuration
VOICE_MODEL_REPO=your-username/ceo-voice-model
REFINEMENT_MODEL=meta-llama/Meta-Llama-3-8B-Instruct

Training Configuration

Key hyperparameters in src/training/train_qlora.py:

Parameter	Default	Description
LoRA rank	64	Rank of LoRA matrices
LoRA alpha	128	Scaling factor
Learning rate	2e-4	Training learning rate
Batch size	4	Per-device batch size
Gradient accumulation	4	Steps before update
Max sequence length	2048	Maximum tokens per example
Epochs	3-5	Training epochs

Evaluation

Run the evaluation suite:

python scripts/evaluate_model.py \
    --model your-username/ceo-voice-model \
    --test-set data/training/validation.jsonl

Metrics

• Vocabulary Overlap: Jaccard similarity with blog corpus (target: >0.7)
• Embedding Similarity: Topic coherence with source material (target: >0.8)
• Factual Accuracy: Claims verified against source (target: >95%)
• Unique Phrase Preservation: CEO's signature phrases detected

Deployment

Hugging Face Spaces

1. Create a new Space on Hugging Face
2. Push the application:

python scripts/push_to_hub.py --space your-username/ai-executive-chatbot

Requirements

• GPU: NVIDIA A10G or T4 recommended
• VRAM: 16GB+ for inference
• Storage: 20GB+ for models

License

Proprietary - Akatsuki AI Technologies

Contact

For questions or support, contact the Akatsuki AI Technologies team.