LLM Fine-Tuning Pipeline Diagram Template

What this template is for

An LLM fine-tuning pipeline diagram shows the path from raw training data to a deployed fine-tuned model. This template lays out the standard stages: dataset preparation, preprocessing and tokenization, the fine-tuning step (LoRA or full SFT) that adapts a base model, an evaluation stage, and deployment to a serving endpoint with a model registry. An evaluation gate loops back to training when metrics don't pass. Use it to design a fine-tuning workflow, document an existing training pipeline, or explain where the base model enters and where evaluation gates deployment.

When to use this template

• Design a fine-tuning pipeline before setting up training infrastructure.
• Explain the difference between LoRA and full fine-tuning by pointing to the training stage.
• Document an existing training pipeline for a new ML engineer.
• Show where the base model is loaded and where the adapted model is registered.
• Plan the evaluation gate that decides whether a checkpoint ships or retrains.
• Compare a fine-tuning pipeline against a RAG approach for adding domain knowledge.

How to use it

1. 1Start with dataset preparation — collecting and cleaning training examples.
2. 2Add preprocessing/tokenization to convert data into model-ready inputs.
3. 3Add the fine-tuning step (LoRA or SFT) and feed the base model into it.
4. 4Add an evaluation stage that measures the fine-tuned model against a benchmark.
5. 5Add an eval gate: pass → deploy and register; fail → loop back to training.
6. 6Add the model registry and serving endpoint as the deployment target.

Quick example