LLM Fine-Tuning Services

Fine-tuning is the process of continuing to train a pre-trained language model on your specific data so it adapts to your task, domain, and output requirements. Use fine-tuning when prompt engineering alone cannot produce consistent output format, when you need significant inference cost reduction at scale (a fine-tuned smaller model can outperform a larger model with a long system prompt), or when domain-specific vocabulary significantly degrades base model performance. Fine-tuning is not always the right answer; start with prompt engineering and RAG first.

OpenAI fine-tuning API supports GPT-4o mini and GPT-3.5 Turbo (hosted fine-tuning, no infrastructure required). Open-source models include Llama 3 (8B, 70B), Mistral 7B, Phi-3, and Gemma (require GPU infrastructure for training). Google Gemini fine-tuning is available via Vertex AI. The right model depends on your budget, data privacy requirements, and accuracy needs. Open-source models eliminate per-token costs and run on your own infrastructure.

For OpenAI fine-tuning, 50 to 100 high-quality examples is the minimum; 500 to 1,000 is recommended for reliable improvement; 5,000 or more for significant domain adaptation. Quality matters more than quantity. For open-source model fine-tuning using LoRA or QLoRA adapters, expect 1,000 to 50,000 examples depending on the degree of adaptation required. We assess your existing data and help curate or generate training examples if your dataset is thin.

LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that trains a small set of adapter weights rather than the full model. It is cheaper in compute and memory than full fine-tuning while achieving comparable results for most tasks. QLoRA extends this with quantization for even lower memory requirements. LoRA is the standard approach for fine-tuning open-source models on modest GPU infrastructure. We use LoRA and QLoRA for open-source model fine-tuning and full fine-tuning only when the task requires it.

We establish a benchmark before fine-tuning. A representative set of inputs with expected outputs is evaluated on your task-specific metrics (accuracy, format compliance, domain terminology usage, output length consistency). The fine-tuned model is evaluated against this benchmark on a held-out test set. We only recommend production deployment when benchmark improvement is statistically significant. Fine-tuning that does not improve over the baseline prompt-engineered base model is not worth the cost.

Fine-tuning project cost covers training data curation, fine-tuning run costs, evaluation, and deployment. For OpenAI fine-tuning (GPT-4o mini or GPT-3.5), the OpenAI training API costs are low ($1 to $10 for typical datasets); the project cost is primarily in data curation and evaluation work ($8,000 to $25,000). For open-source model fine-tuning with infrastructure setup, expect $20,000 to $60,000 including GPU compute, deployment infrastructure, and evaluation framework.