Fine-Tuning Steps of LLMs for Custom Domains
Step 1: Define Objective
Examples:
- Medical chatbot
- Legal assistant
- Financial QA
- Manufacturing copilots
Key question:
- Generation?
- Classification?
- Tool calling?
- Extraction?
Step 2: Collect Data
Sources:
- PDFs
- APIs
- CRM
- SQL
- Emails
- Documentation
Step 3: Clean Data
Remove:
- Duplicates
- Corrupted text
- Hallucinated labels
- Bad OCR
Step 4: Structure Dataset
Formats:
- Instruction tuning
- Chat format
- Completion format
Example:
{
“instruction”: “Explain invoice mismatch”,
“input”: “…”,
“output”: “…”
}
Step 5: Data Chunking
Important for:
- RAG
- Long-context training
Strategies:
- Semantic chunking
- Recursive chunking
- Token-aware chunking
Step 6: Choose Base Model
Factors:
- License
- Context length
- GPU requirements
- Language support
Popular choices:
- Llama
- Mistral
- Gemma
- Qwen
Step 7: Tokenization
Tokenizer mismatch can destroy performance.
Always use:
- Native tokenizer
- Consistent preprocessing
Step 8: Select Fine-Tuning Strategy
| Strategy | Use Case |
| Full Fine-Tuning | Large budget |
| LoRA | Most common |
| QLoRA | Low VRAM |
| Prefix Tuning | Lightweight |
| DPO | Preference optimization |
Step 9: Hyperparameter Tuning
Critical parameters:
- Learning rate
- Batch size
- Rank
- Warmup
- Epochs
Step 10: Evaluation
Metrics:
- BLEU
- ROUGE
- Exact Match
- Hallucination rate
- RAGAS
- Human evaluation
Step 11: Safety Alignment
Add:
- Guardrails
- Refusal tuning
- Toxicity filtering
Step 12: Deployment
Typical serving:
- vLLM
- TGI
- TensorRT-LLM
w you to monitor and control energy usage, ensuring that your environmental impact remains minimal.
