Fine-Tuning Guide
🚀
Getting Started
3
🧪
Methods & Techniques
1
⚙️
Implementation
1
🌐
Deployment
2
☁️ Cloud Fine-Tuning Options
Compare pricing, features, and capabilities of major cloud providers for LLM fine-tuning.
💡 Cost Optimization Tips
Money-Saving Strategies
- • Use spot instances for interruptible workloads
- • Choose QLoRA over full fine-tuning
- • Use gradient accumulation to reduce batch size
- • Monitor training and stop early if overfitting
- • Use smaller models when possible
- • Take advantage of reserved pricing
Platform Selection
- • Vast.ai: Cheapest for single GPU
- • Together AI: Best for API workflows
- • RunPod: Most user-friendly
- • Hyperstack: Best for long-term projects
- • Major clouds: Enterprise compliance
- • Compare total cost including data transfer
☁️ Cloud Fine-Tuning Options
Compare pricing, features, and capabilities of major cloud providers for LLM fine-tuning.
💡 Cost Optimization Tips
Money-Saving Strategies
- • Use spot instances for interruptible workloads
- • Choose QLoRA over full fine-tuning
- • Use gradient accumulation to reduce batch size
- • Monitor training and stop early if overfitting
- • Use smaller models when possible
- • Take advantage of reserved pricing
Platform Selection
- • Vast.ai: Cheapest for single GPU
- • Together AI: Best for API workflows
- • RunPod: Most user-friendly
- • Hyperstack: Best for long-term projects
- • Major clouds: Enterprise compliance
- • Compare total cost including data transfer
Fine-Tuning Guide
closedFine-Tuning Guide
🚀
Getting Started
3
🧪
Methods & Techniques
1
⚙️
Implementation
1
🌐
Deployment
2