What is LLM fine-tuning?

Fine-tuning adapts pretrained large language models to specific tasks or domains by updating their weights, often improving output quality while reducing prompt length, latency, and inference costs compared with general-purpose API models. It is suited to tasks such as structured-data generation, style control, and domain-specific classification, while retrieval-augmented generation may be preferable or complementary when external knowledge must be supplied at runtime. Fine-tuning can be performed through low-code services such as OpenAI and Predibase or configurable infrastructure including Modal, Google Colab, and AWS SageMaker, using frameworks such as Hugging Face Transformers, TRL, and Axolotl. The workflow involves selecting and testing an appropriate base model, preparing high-quality training and validation data with consistent prompt formats and tokenization, configuring and monitoring training, and applying efficiency techniques such as LoRA, QLoRA, quantization, and multi-GPU distributed training through DeepSpeed, FSDP, or Accelerate. Modal is presented as a serverless option for packaging training environments, accessing on-demand GPUs, and running distributed fine-tuning jobs for open-source models such as Llama and Mistral.

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