βοΈ Fine-Tuning Parameters
π° Cost Estimate
π About AI Fine-Tuning
What is LLM fine-tuning?
LLM fine-tuning is continuing the training of a pre-trained large language model on task-specific data, so the model performs better on a specific task such as text analysis, code generation, and more.
Fine-Tuning Methods
- API fine-tuning β Use platforms such as OpenAI API. No GPU required, priced per token, simple and fast
- LoRA fine-tuning β Self-hosted, only updates a few parameters, cutting training cost by 70-90% with performance close to full fine-tuning
- QLoRA fine-tuning β LoRA + 4-bit quantization, even lower memory requirements, fits consumer-level GPUs
- Full fine-tuning β Updates all parameters, best effect but highest cost, requires multiple GPUs
Cost Optimization Tips
- Use LoRA/QLoRA to reduce training cost by 70-90%
- Data quality matters more than quantity; 500 high-quality samples can outperform 5,000 low-quality samples
- Start with a small dataset, confirm the effect, then scale up
- Consider RAG for knowledge-based scenarios instead of fine-tuning
- Inference cost grows with usage; long-running workloads can exceed training cost
π FAQ
What is LLM fine-tuning?
Fine-tuning is continuing the training of a pre-trained large language model on task-specific data so the model performs better on a specific task, such as text classification, code generation, or custom output formats.
Which is better: fine-tuning or RAG?
RAG fits tasks combined with a knowledge base and retrieval; fine-tuning fits specific output formats or styles. If you only need model-specific knowledge, RAG is better; if you need to modify the output format, fine-tuning is more effective. Many scenarios combine both.
What is the difference between LoRA fine-tuning and full fine-tuning?
LoRA only updates a small number of parameters, reducing training cost by 70-90% with performance close to full fine-tuning. Full fine-tuning updates all parameters, giving the best effect but at the highest cost, typically requiring multiple GPUs.
How much data do I need to fine-tune a model?
A few hundred to a few thousand high-quality samples are often enough for many tasks. Start with a small dataset, confirm the effect, then scale up.
Is my data uploaded to a server?
No. All cost calculations run locally in your browser. No data is sent to any server.