:INFO
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.


:COUNTER.half
1,000 Examples
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:COUNTER.half
4 GB VRAM

:PATH
Prepare Data
Collect and clean domain-specific text. Format as instruction-response pairs in JSONL. Aim for at least 1,000 high-quality examples.

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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.

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Evaluate
Test on held-out examples. Compare outputs to the base model. Measure accuracy, tone, and format adherence against your target criteria.

:CHECKLIST
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

:NOTE
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.


:LINK
https://github.com/OpenAccess-AI-Collective/axolotl
Axolotl: streamlined fine-tuning for open source LLMs