Fine-Tuning Guide
🚀
Getting Started
3
🧪
Methods & Techniques
1
⚙️
Implementation
1
🌐
Deployment
2
🏠 Local Fine-Tuning Setup
Fine-tune models on your own hardware - from consumer GPUs to enterprise setups.
⚡ Performance Optimization
Memory Optimization
- • Use QLoRA (4-bit) over LoRA
- • Enable gradient checkpointing
- • Reduce sequence length
- • Use micro-batching
- • Offload optimizer to CPU
Speed Optimization
- • Use Flash Attention 2
- • Enable mixed precision (FP16)
- • Use torch.compile (PyTorch 2.0+)
- • Optimize data loading
- • Use efficient optimizers
System Setup
- • Fast NVMe SSD storage
- • Adequate system RAM
- • Good cooling/power supply
- • Monitor GPU temperatures
- • Use containers for consistency
🏠 Local Fine-Tuning Setup
Fine-tune models on your own hardware - from consumer GPUs to enterprise setups.
⚡ Performance Optimization
Memory Optimization
- • Use QLoRA (4-bit) over LoRA
- • Enable gradient checkpointing
- • Reduce sequence length
- • Use micro-batching
- • Offload optimizer to CPU
Speed Optimization
- • Use Flash Attention 2
- • Enable mixed precision (FP16)
- • Use torch.compile (PyTorch 2.0+)
- • Optimize data loading
- • Use efficient optimizers
System Setup
- • Fast NVMe SSD storage
- • Adequate system RAM
- • Good cooling/power supply
- • Monitor GPU temperatures
- • Use containers for consistency
Fine-Tuning Guide
closedFine-Tuning Guide
🚀
Getting Started
3
🧪
Methods & Techniques
1
⚙️
Implementation
1
🌐
Deployment
2