modelforge-finetuning 2.0.0

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

ModelForge 🔧⚡

Finetune LLMs on your laptop’s GPU—no code, no PhD, no hassle.

🚀 Features

• GPU-Powered Finetuning: Optimized for NVIDIA GPUs (even 4GB VRAM).
• One-Click Workflow: Upload data → Pick task → Train → Test.
• Hardware-Aware: Auto-detects your GPU/CPU and recommends models.
• React UI: No CLI or notebooks—just a friendly interface.

📖 Supported Tasks

• Text-Generation: Generates answers in the form of text based on prior and fine-tuned knowledge. Ideal for use cases like customer support chatbots, story generators, social media script writers,[...]
• Summarization: Generates summaries for long articles and texts. Ideal for use cases like news article summarization, law document summarization, and medical article summarization.
• Extractive Question Answering: Finds the answers relevant to a query from a given context. Best for use cases like Retrieval Augmented Generation (RAG), and enterprise document search (for examp[...]

Installation

Prerequisites

• Python==3.11.x: Ensure you have Python installed.
• NVIDIA GPU: Recommended VRAM >= 6GB.
• CUDA: Ensure CUDA is installed and configured for your GPU.
• HuggingFace Account: Create an account on Hugging Face and generate a finegrained access token.

Steps

1. Install the Package:

   pip install modelforge-finetuning
   

2. Set HuggingFace API Key in environment variables:
   Linux:

   export HUGGINGFACE_TOKEN=your_huggingface_token
   

   Windows Powershell:

   $env:HUGGINGFACE_TOKEN="your_huggingface_token"
   

   Windows CMD:

   set HUGGINGFACE_TOKEN=your_huggingface_token
   

   Or use a .env file:

   echo "HUGGINGFACE_TOKEN=your_huggingface_token" > .env
   

3. Install Appropriate CUDA version for PyTorch:

   • Navigate to the PyTorch installation page and select the appropriate CUDA version for your system.
   • Install PyTorch with the correct CUDA version. For example, for CUDA 12.6 on Windows, you can use:

    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
   

4. Run the Application:

   modelforge run
   

5. Done!: Navigate to http://localhost:8000 in your browser and get started!

Running the Application Again in the Future

1. Start the Application:

   modelforge run
   

2. Navigate to the App:
   Open your browser and go to http://localhost:8000.

Stopping the Application

To stop the application and free up resources, press Ctrl+C in the terminal running the app.

📂 Dataset Format

{"input": "Enter a really long article here...", "output": "Short summary."},
{"input": "Enter the poem topic here...", "output": "Roses are red..."}

🤝 Contributing Model Recommendations

ModelForge uses a modular configuration system for model recommendations. Contributors can easily add new recommended models by adding configuration files to the model_configs/ directory. Each hardw[...]

See the Model Configuration Guide for detailed instructions on how to add new model recommendations.

🛠 Tech Stack

• transformers + peft (LoRA finetuning)
• bitsandbytes (4-bit quantization)
• React (UI)
• FastAPI (Backend)
• Python (Backend)
• React.JS (Frontend)