modelforge-finetuning 2.2.0

ModelForge: A no-code toolkit for fine-tuning HuggingFace models

ModelForge 🔧⚡

Fine-tune LLMs on your laptop's GPU—no code, no PhD, no hassle.

ModelForge v2.1 is a complete architectural overhaul bringing 2x faster training, modular providers, advanced strategies, and production-ready code quality.

✨ What's New in v2.1

• 🚀 2x Faster Training with Unsloth provider
• 🧩 Multiple Providers: HuggingFace, Unsloth (more coming!)
• 🎯 Advanced Strategies: SFT, QLoRA, RLHF, DPO
• 📊 Built-in Evaluation with task-specific metrics
• 🖥️ Interactive CLI Wizard (modelforge cli) for headless/SSH environments
• 📦 Optional Quantization — bitsandbytes moved to [quantization] extra

See What's New in v2.1 →

🚀 Features

• GPU-Powered Fine-Tuning: Optimized for NVIDIA GPUs (even 4GB VRAM)
• One-Click Workflow: Upload data → Configure → Train → Test
• Hardware-Aware: Auto-detects GPU and recommends optimal models
• No-Code UI: Beautiful React interface, or use the CLI wizard for headless environments
• Multiple Providers: HuggingFace (standard) or Unsloth (2x faster)
• Advanced Strategies: SFT, QLoRA, RLHF, DPO support
• Automatic Evaluation: Built-in metrics for all tasks

📖 Supported Tasks

• Text Generation: Chatbots, instruction following, code generation, creative writing
• Summarization: Document condensing, article summarization, meeting notes
• Question Answering: RAG systems, document search, FAQ bots

🎯 Quick Start

Prerequisites

• Python 3.11.x (Python 3.12 not yet supported)
• NVIDIA GPU with 4GB+ VRAM (6GB+ recommended)
• CUDA installed and configured
• HuggingFace Account with access token (Get one here)
• Linux or Windows operating system

⚠️ macOS is NOT supported. ModelForge requires NVIDIA CUDA which is not available on macOS. Use Linux or Windows with NVIDIA GPU.

Windows Users: See Windows Installation Guide for platform-specific instructions, especially for Unsloth support.

Installation

# Install ModelForge
pip install modelforge-finetuning

# Optional extras
pip install modelforge-finetuning[cli]           # CLI wizard
pip install modelforge-finetuning[quantization]   # 4-bit/8-bit quantization

# Install PyTorch with CUDA support
# Visit https://pytorch.org/get-started/locally/ for your CUDA version
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

Set HuggingFace Token

Linux:

export HUGGINGFACE_TOKEN=your_token_here

Windows PowerShell:

$env:HUGGINGFACE_TOKEN="your_token_here"

Or use .env file:

echo "HUGGINGFACE_TOKEN=your_token_here" > .env

Run ModelForge

modelforge          # Launch web UI
modelforge cli      # Launch CLI wizard (headless/SSH)

Open your browser to http://localhost:8000 and start training!

Full Quick Start Guide →

📚 Documentation

Getting Started

• Quick Start Guide - Get up and running in 5 minutes
• What's New in v2.1 - Major features and improvements

Installation

• Windows Installation - Complete Windows setup (including WSL and Docker)
• Linux Installation - Linux setup guide
• Post-Installation - Initial configuration

Configuration & Usage

• Configuration Guide - All configuration options
• Dataset Formats - Preparing your training data
• Training Tasks - Understanding different tasks
• Hardware Profiles - Optimizing for your GPU

Providers

• Provider Overview - Understanding providers
• HuggingFace Provider - Standard HuggingFace models
• Unsloth Provider - 2x faster training

Training Strategies

• Strategy Overview - Understanding strategies
• SFT Strategy - Standard supervised fine-tuning
• QLoRA Strategy - Memory-efficient training
• RLHF Strategy - Reinforcement learning
• DPO Strategy - Direct preference optimization

API Reference

• REST API - Complete API documentation
• Training Config Schema - Configuration options

Troubleshooting

• Common Issues - Frequently encountered problems
• Windows Issues - Windows-specific troubleshooting
• FAQ - Frequently asked questions

Contributing

• Contributing Guide - How to contribute
• Architecture - Understanding the codebase
• Model Configurations - Adding model recommendations

📖 Full Documentation Index →

🔧 Platform Support

Platform	HuggingFace Provider	Unsloth Provider	Notes
Linux	✅ Full support	✅ Full support	Recommended
Windows (Native)	✅ Full support	❌ Not supported	Use WSL or Docker for Unsloth
WSL 2	✅ Full support	✅ Full support	Recommended for Windows users
Docker	✅ Full support	✅ Full support	With NVIDIA runtime

Platform-Specific Installation Guides →

⚠️ Important Notes

Windows Users

Unsloth provider is NOT supported on native Windows. For 2x faster training with Unsloth:

1. Option 1: WSL (Recommended) - WSL Installation Guide
2. Option 2: Docker - Docker Installation Guide

The HuggingFace provider works perfectly on native Windows.

Unsloth Constraints

When using Unsloth provider, you MUST specify a fixed max_sequence_length:

{
  "provider": "unsloth",
  "max_seq_length": 2048  // ✅ Required - cannot be -1
}

Auto-inference (max_seq_length: -1) is NOT supported with Unsloth.

Learn more about Unsloth →

📂 Dataset Format

ModelForge uses JSONL format. Each task has specific fields:

Text Generation:

{"input": "What is AI?", "output": "AI stands for Artificial Intelligence..."}
{"input": "Explain ML", "output": "Machine Learning is a subset of AI..."}

Summarization:

{"input": "Long article text...", "output": "Short summary."}

Question Answering:

{"context": "Document text...", "question": "What is X?", "answer": "X is..."}

Complete Dataset Format Guide →

🤝 Contributing

We welcome contributions! ModelForge's modular architecture makes it easy to:

• Add new providers - Just 2 files needed
• Add new strategies - Just 2 files needed
• Add model recommendations - Simple JSON configs
• Improve documentation
• Fix bugs and add features

Contributing Guide →

Adding Model Recommendations

ModelForge uses modular configuration files for model recommendations. See the Model Configuration Guide for instructions on adding new recommended models.

🛠 Tech Stack

• Backend: Python, FastAPI, SQLAlchemy
• Frontend: React.js
• ML: PyTorch, Transformers, PEFT, TRL
• Training: LoRA, QLoRA, bitsandbytes (optional)
• Providers: HuggingFace Hub, Unsloth

Results on NVIDIA RTX 3090. Your results may vary.

📜 License

BSD License - see LICENSE file for details.

🙏 Acknowledgments

• HuggingFace for Transformers and model hub
• Unsloth AI for optimized training kernels
• The open-source ML community

📧 Support

• Documentation: https://github.com/forgeopus/modelforge/tree/main/docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• PyPI: modelforge-finetuning

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ModelForge v2.1 - Making LLM fine-tuning accessible to everyone 🚀

Get Started → | Documentation → | GitHub →