Fine-Tuning Guide
Getting Started
Methods & Techniques
Implementation
Deployment
Full Fine-Tuning
Update all model parameters. Best performance but requires most memory.
LoRA
Low-Rank Adaptation. Train small adapter layers, freeze main model.
QLoRA
Quantized LoRA. 4-bit quantization + LoRA adapters for maximum efficiency.
🔬 Advanced Techniques (2025)
DoRA (Weight-Decomposed LoRA)
Decomposes weights into magnitude and direction, then applies LoRA to both. Achieves better performance than standard LoRA.
LoRA+ & AdaLoRA
LoRA+: Different learning rates for A and B matrices (2x faster). AdaLoRA: Adaptive rank allocation based on layer importance.
RoSA (Robust Sparse Adaptation)
Combines low-rank and sparse updates. Better accuracy than LoRA with same parameter budget.
MoELoRA
Mixture of Experts LoRA with contrastive learning to encourage expert specialization.
GRPO & DPO
Generalized Reward Process Optimization and Direct Preference Optimization for alignment without RL complexity.
Quantization-Aware Training
Train models optimized for quantized inference. Better performance than post-training quantization.
Full Fine-Tuning
Update all model parameters. Best performance but requires most memory.
LoRA
Low-Rank Adaptation. Train small adapter layers, freeze main model.
QLoRA
Quantized LoRA. 4-bit quantization + LoRA adapters for maximum efficiency.
🔬 Advanced Techniques (2025)
DoRA (Weight-Decomposed LoRA)
Decomposes weights into magnitude and direction, then applies LoRA to both. Achieves better performance than standard LoRA.
LoRA+ & AdaLoRA
LoRA+: Different learning rates for A and B matrices (2x faster). AdaLoRA: Adaptive rank allocation based on layer importance.
RoSA (Robust Sparse Adaptation)
Combines low-rank and sparse updates. Better accuracy than LoRA with same parameter budget.
MoELoRA
Mixture of Experts LoRA with contrastive learning to encourage expert specialization.
GRPO & DPO
Generalized Reward Process Optimization and Direct Preference Optimization for alignment without RL complexity.
Quantization-Aware Training
Train models optimized for quantized inference. Better performance than post-training quantization.