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Fine-tuning Methods Diagram

Fine-tuning Overview

IdeaWeaver provides comprehensive support for model fine-tuning with multiple approaches:

Available Methods

Full Fine-tuning

  • Complete model parameter updates
  • Maximum customization potential
  • Higher resource requirements

LoRA (Low-Rank Adaptation)

  • Parameter-efficient fine-tuning
  • Reduced memory footprint
  • Faster training times

QLoRA (Quantized LoRA)

  • Memory-efficient fine-tuning
  • 4-bit quantization support
  • Ideal for large models

Key Features

  • Gradient Checkpointing: Memory optimization for large models
  • Gradient Accumulation: Effective batch size control
  • Mixed Precision Training: FP16/BF16 support
  • Multi-GPU Support: Distributed training capabilities
  • Experiment Tracking: Integration with MLflow & W&B

Prerequisites

  • Python 3.12+
  • CUDA-compatible GPU (for full fine-tuning)
  • Sufficient disk space for model checkpoints
  • Dataset in supported format (JSON, CSV, etc.)

Getting Started

See the Commands page for detailed usage instructions and Examples for practical use cases.