Fine-tuning Methods

Fine-tuning Methods Overview

Ideaweaver supports four main fine-tuning approaches, each designed for different use cases and resource constraints:

1. Full Fine-tuning

• Complete Model Update: Updates all model parameters
• Higher Memory Usage: Requires more GPU memory
• Best Performance: Achieves optimal results but at higher resource cost

2. LoRA Fine-tuning

• Low-Rank Adaptation: Uses efficient parameter updates
• Memory Efficient: Requires significantly less memory
• Faster Training: Quicker training with good performance

3. QLoRA Fine-tuning

• Quantized LoRA: Combines quantization with LoRA
• Ultra Memory Efficient: Minimal memory requirements
• 4-bit Quantization: Uses 4-bit precision for maximum efficiency

4. Train & Quantize

• Training + Quantization: Combines both processes
• Multiple Quant Methods: Supports int8 and gguf quantization
• Optimized Deployment: Ready for production deployment

Color Legend

• 🟢 Dark Green: Main Ideaweaver node
• 🔵 Blue: Fine-tuning methods
• 🟠 Orange: Features and characteristics

When to Use Each Method

1. Full Fine-tuning: When you have sufficient GPU memory and need the best possible performance
2. LoRA: When you want a good balance between performance and resource usage
3. QLoRA: When working with limited GPU memory or large models
4. Train & Quantize: When you need an optimized model for deployment