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lilbablo
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humigencev2

Text Generation
Transformers
English
finetuning
lora
qlora
unsloth
gpu
distributed
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🧠 Humigence CLI

Your AI. Your pipeline. Zero code.

A complete MLOps suite built for makers, teams, and enterprises. Humigence provides zero-config, GPU-aware fine-tuning with surgical precision and complete reproducibility.

✨ Key Features

  • 🎯 Interactive Wizard: Step-by-step configuration with Basic/Advanced modes
  • 🖥️ Smart GPU Detection: Automatic detection and selection of available GPUs
  • 🚀 Dual-GPU Training: Multi-GPU support with Unsloth + TorchRun
  • 🧪 Training Recipes: QLoRA (4-bit), LoRA (FP16/BF16), Full Fine-tuning
  • 📊 Intelligent Batching: Auto-fit batch size to available VRAM
  • 🔄 Complete Reproducibility: Config snapshots and reproduce scripts
  • 📈 Built-in Evaluation: Curated prompts and quality gates
  • 📦 Artifact Export: Structured outputs with run summaries

🚀 Quick Start

Prerequisites

  • GPU: NVIDIA GPU with CUDA support (RTX 5090, RTX 4080, etc.)
  • RAM: 8GB+ recommended
  • Storage: 10GB+ for models and datasets
  • Python: 3.8+ with PyTorch

Installation 

# Clone the repository
git clone https://github.com/your-username/humigence.git
cd humigence

# Install dependencies
pip install -r requirements.txt

# Set up Unsloth (required for training)
python3 training/unsloth/setup_humigence_unsloth.py

# Launch the interactive wizard
python3 cli/main.py

Basic Usage 

# Launch the interactive wizard
python3 cli/main.py

# The wizard will guide you through:
# 1. Model selection
# 2. Dataset configuration  
# 3. Training parameters
# 4. GPU selection (single or multi-GPU)
# 5. Launch training

🎯 Training Workflow

1. Interactive Setup

The Humigence wizard guides you through:

  • Setup Mode: Basic (essential config) or Advanced (full control)
  • Hardware Detection: Automatic GPU, CPU, and memory detection
  • Model Selection: Choose from supported models or custom paths
  • Dataset Loading: Auto-detection from ~/humigence_data/ or custom paths
  • Training Recipe: QLoRA, LoRA, or Full Fine-tuning
  • GPU Selection: Single-GPU auto-selection or multi-GPU prompting

2. GPU Selection

Humigence intelligently handles GPU selection:

  • Single GPU: Automatically selects and uses the available GPU
  • Multiple GPUs: Prompts you to choose:
    🔧 Training Mode:
> Multi-GPU Training (all available GPUs)
  Single GPU Training (choose specific GPU)

3. Training Execution 

🚀 Humigence Training Starting...
✅ Configuration Loaded: [all settings]
🖥️ GPU Detection: 2x RTX 5090 detected
🔧 Training Mode: Multi-GPU Training
📦 Loading model: Qwen/Qwen2.5-0.5B
✅ LoRA adapters applied
📚 Loading dataset: wikitext2 (10,000 samples)
🚀 Starting training with TorchRun...
✅ Training complete — adapters saved.

📊 Supported Models

  • Qwen/Qwen2.5-0.5B: 77M parameters (recommended for testing)
  • microsoft/Phi-2: 839M parameters
  • TinyLlama/TinyLlama-1.1B-Chat-v1.0: 369M parameters
  • Custom Models: Any HuggingFace model or local path

🗂️ Dataset Support

  • JSONL Format: Line-by-line JSON with instruction/output pairs
  • Auto-Detection: Scans ~/humigence_data/ directory
  • Custom Paths: Specify any local dataset file
  • Sample Datasets: Includes demo datasets for testing

Dataset Format 

{"instruction": "What is machine learning?", "output": "Machine learning is a subset of artificial intelligence..."}
{"instruction": "Explain quantum computing", "output": "Quantum computing uses quantum mechanical phenomena..."}

🖥️ Hardware Requirements

Minimum Requirements

  • GPU: NVIDIA GPU with 8GB+ VRAM
  • RAM: 16GB+ system RAM
  • Storage: 20GB+ free space

Recommended Setup

  • GPU: RTX 4080/4090/5090 or better
  • RAM: 32GB+ system RAM
  • Storage: 50GB+ free space

Multi-GPU Support

  • Dual-GPU: RTX 5090 + RTX 5090 (tested)
  • Memory: 16GB+ VRAM per GPU recommended
  • Training: Automatic TorchRun distribution

📁 Project Structure 

humigence/
├── cli/
│   ├── main.py              # Main CLI entry point
│   ├── config_wizard.py     # Interactive configuration wizard
│   └── lora_wizard.py       # LoRA-specific wizard
├── training/
│   └── unsloth/            # Unsloth integration
│       ├── wizard.py       # Unsloth training wizard
│       └── train_lora_dual.py  # Multi-GPU training script
├── pipelines/
│   └── lora_trainer.py     # Training pipeline
├── utils/
│   ├── device.py           # Hardware detection
│   ├── dataset_loader.py   # Dataset utilities
│   └── validators.py       # Data validation
├── config/
│   └── default_config.json # Default configuration
└── runs/                   # Training outputs
    └── humigence/
        ├── config.snapshot.json
        ├── adapters/       # LoRA weights
        └── artifacts.zip   # Complete export

🔧 Configuration

Basic Mode (Recommended)

Essential configuration with sensible defaults:

  • Learning Rate: 2e-4
  • Epochs: 1
  • Gradient Accumulation: 4
  • LoRA Rank: 16
  • LoRA Alpha: 32

Advanced Mode

Full control over all parameters:

  • LoRA configuration (rank, alpha, dropout)
  • Training hyperparameters
  • Data processing options
  • Evaluation settings

🚀 Training Modes

Single-GPU Training 

# Automatically selected when 1 GPU detected
🔧 Single GPU detected - using GPU 0: RTX 5090
🚀 Launching single-GPU training...

Multi-GPU Training 

# Prompts when multiple GPUs detected
🔧 2 GPUs detected - choose training mode
> Multi-GPU Training (all available GPUs)
  Single GPU Training (choose specific GPU)

📈 Evaluation & Monitoring

Built-in Evaluation

  • Curated Prompts: 5 diverse evaluation questions
  • Model Inference: Generation with temperature sampling
  • Quality Gates: Loss thresholds and evaluation metrics
  • Status Tracking: ACCEPTED.txt or REJECTED.txt files

Run Monitoring 

# View training progress
tail -f runs/humigence/training.log

# Check evaluation results
cat runs/humigence/eval_results.jsonl

# View run summary
cat runs/humigence/run_summary.json

🔄 Reproducibility

Every training run generates:

  • Config Snapshot: Complete configuration in JSON
  • Reproduce Script: One-click rerun capability
  • Artifact Archive: Complete export of all outputs
  • Run Summary: Structured metadata for tracking
# Rerun any training
./runs/humigence/reproduce.sh

# Or use the config directly
python3 training/unsloth/train_lora_dual.py --config runs/humigence/config.snapshot.json

🛠️ Development

Dependencies

Core dependencies are pinned for stability:

transformers>=4.41.0,<5.0.0
torch>=2.1.0
unsloth @ git+https://github.com/unslothai/unsloth.git
rich>=13.0.0
inquirer>=3.1.0

Local Development 

# Install in development mode
pip install -e .

# Run tests
python3 -m pytest tests/

# Run specific test
python3 test_gpu_selection.py

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

Quick Contribution Guide

  1. Fork the repository
  2. Create a feature branch: git checkout -b feature/amazing-feature
  3. Make your changes
  4. Add tests if applicable
  5. Commit your changes: git commit -m 'Add amazing feature'
  6. Push to the branch: git push origin feature/amazing-feature
  7. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Unsloth for fast LoRA training
  • HuggingFace for the transformers library
  • Microsoft for PEFT and LoRA implementations
  • The open-source ML community

🆚 Comparison with Other Tools

Feature Humigence CLI Other Tools
Setup Interactive wizard Manual config
GPU Detection Automatic Manual
Multi-GPU Built-in TorchRun Complex setup
Reproducibility Complete snapshots Partial
Evaluation Built-in prompts External tools
Artifacts Structured export Manual collection

🐛 Troubleshooting

Common Issues

GPU not detected:

# Check CUDA installation
python3 -c "import torch; print(torch.cuda.is_available())"

# Check GPU visibility
nvidia-smi

Out of memory:

# Reduce batch size in config
# Or use QLoRA for memory efficiency

Training fails:

# Check logs
cat runs/humigence/training.log

# Verify dataset format
head -5 ~/humigence_data/your_dataset.jsonl

Getting Help

🗺️ Roadmap

Current Features ✅

  • Interactive configuration wizard
  • Single and multi-GPU training
  • QLoRA and LoRA support
  • Built-in evaluation
  • Complete reproducibility

Coming Soon 🚧

  • RAG implementation
  • EnterpriseGPT integration
  • Batch inference
  • Context length optimization
  • Web UI interface
  • Model serving

Future Features 🔮

  • Distributed training across nodes
  • Advanced evaluation metrics
  • Model compression
  • Deployment automation

Built with ❤️ for the AI community

Humigence — Your AI. Your pipeline. Zero code.

📊 Stats

GitHub stars GitHub forks GitHub issues GitHub license Python version PyTorch CUDA

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Dataset used to train lilbablo/humigencev2

Evaluation results

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