Fine-Tuning an LLM for Your Niche
LoRA and QLoRA make it practical to fine-tune an open source language model on domain-specific data using consumer hardware. The result is a model that speaks your niche's language and follows your formatting conventions.
1,000Examples
4GB VRAM
Prepare Data
Collect and clean domain-specific text. Format as instruction-response pairs in JSONL. Aim for at least 1,000 high-quality examples.
Train
Use Axolotl or Hugging Face TRL with QLoRA settings. Run on a single GPU or use a cloud provider for a short training run.
Evaluate
Test on held-out examples. Compare outputs to the base model. Measure accuracy, tone, and format adherence against your target criteria.
Fine-Tuning Checklist
Define the target task and evaluation criteria
Collect and clean training data
Format data as instruction-response JSONL pairs
Choose base model and LoRA rank settings
Run training with validation split
Evaluate on test set
Merge adapter weights into base model
Deploy with Ollama or vLLM
QLoRA reduces memory requirements by quantizing the base model to 4-bit precision during training. You can fine-tune a 13B model on a single 24 GB GPU without quality loss compared to full fine-tuning.
