What is Fine-Tuning? - Definition & Meaning

Definition

Fine-tuning is the process of further training a pre-trained AI model (such as an LLM) on a specific dataset to specialize it for a particular task or domain. The model retains its general knowledge but becomes sharpened for the desired style, tone, or expertise.

Technical explanation

Fine-tuning adjusts the weights of a pre-trained model via supervised learning on a dataset of input-output pairs. There are several fine-tuning methods: full fine-tuning (all parameters are adjusted but requires significant GPU memory), LoRA (Low-Rank Adaptation) and QLoRA which only add small adapter layers drastically reducing training compute requirements, and RLHF (Reinforcement Learning from Human Feedback) for aligning with human preferences. The training dataset typically consists of hundreds to thousands of examples of desired conversations or tasks. Hyperparameters such as learning rate, batch size, epochs, and warmup steps must be carefully tuned to prevent overfitting. Evaluation uses hold-out test sets and task-specific metrics (accuracy, F1-score, BLEU, human evaluation). Platforms like OpenAI, Anyscale, and Hugging Face offer fine-tuning as a service, while local fine-tuning is possible with frameworks like Hugging Face Transformers, Axolotl, and Unsloth.

How OpenClaw Installeren applies this

OpenClaw Installeren supports both standard and fine-tuned models in your AI assistant stack. For most applications, we recommend RAG (faster, cheaper, more flexible), but when you need a specific writing style, tone, or behavioral pattern, we can integrate a fine-tuned model into your deployment. The configured VPS supports local inference of fine-tuned open-source models via Ollama.

Practical examples

• A law firm fine-tuning an LLM on thousands of legal documents so the model correctly uses legal jargon and generates answers in the formal style the industry requires.
• A customer service team fine-tuning a model on historical support conversations so the AI assistant adopts the specific tone and terminology of the brand.
• A medical company fine-tuning an open-source LLM on medical literature to create a specialized assistant that answers medical questions more accurately than a generic model.

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