Fine-Tuning LLMs — Custom Model Training & Adaptation
Teach an open model your domain, voice, and tasks.
When prompting and RAG aren't enough — you need consistent format, domain jargon, a specific voice, or lower inference cost — fine-tuning adapts an open-weight model to your exact task. I handle the full fine-tuning lifecycle: dataset curation, LoRA/QLoRA or full training, rigorous evaluation, and deployment, so you own a model tuned to your business.
When to fine-tune (and when not to)
Fine-tuning is powerful but not always the answer. I help you make the call honestly — often prompt engineering and RAG solve the problem faster and cheaper. Fine-tuning wins when you need consistency, style, structured output, latency, or cost that in-context methods can't reach.
- Dataset curation, cleaning, and instruction formatting
- Parameter-efficient tuning with LoRA and QLoRA for cost efficiency
- Full fine-tuning when the task demands it
- Preference tuning (DPO) to align tone and behavior
- Held-out evaluation to prove the tuned model beats the baseline
- Quantization and deployment for cost-effective inference
How I run a fine-tuning engagement
Everything is measured against a frozen evaluation set so we can prove the fine-tuned model actually improves on the base model for your task. I curate and version datasets, run efficient LoRA/QLoRA training, and deliver a quantized, deployable model plus the eval harness to keep it honest over time.
Technologies I use
Frequently asked questions
What is LLM fine-tuning?
Fine-tuning further trains a pre-trained language model on your own examples so it specializes in your domain, format, or style. Modern techniques like LoRA and QLoRA make this affordable by updating only a small set of adapter weights instead of the whole model.
Should I fine-tune or use RAG?
They solve different problems and are often combined. RAG gives the model knowledge (facts it can retrieve); fine-tuning gives it behavior (format, tone, task consistency, lower cost). I recommend RAG first for knowledge-heavy use cases and fine-tuning when you need reliable structure or cheaper inference.
What is the difference between LoRA and QLoRA?
LoRA trains small adapter matrices instead of the full model, cutting memory and cost. QLoRA goes further by quantizing the base model to 4-bit during training, letting you fine-tune large models on a single GPU. I choose based on model size, budget, and quality targets.
Ready to build with fine-tuning llms?
Let's scope your project and turn it into a production system.
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