Fine-tuning allows you to customize AI models for your specific use case.
Step-by-step guide to fine-tuning DeepSeek models.
What is Fine-tuning?
Training a pre-trained model on your specific data to improve performance.
When to Fine-tune
✅ Domain-specific tasks
✅ Custom output formats
✅ Improved accuracy needs
✅ Reduced prompt engineering
Preparing Data
Format your data as JSON with input-output pairs:
{“input”: “question”, “output”: “answer”}
Fine-tuning Steps
1. Collect training data
2. Format for training
3. Upload to platform
4. Start fine-tuning job
5. Monitor progress
6. Test fine-tuned model
Cost Considerations
Fine-tuning costs vary by model size and data volume.
Best Practices
✅ Use high-quality data
✅ Start with small dataset
✅ Validate results
Conclusion
Fine-tuning creates custom models for better performance!