LLM Fine-Tuning Pipeline Diagram Examples

These fine-tuning pipeline examples show how the same dataset-train-eval-deploy backbone changes with the training method — parameter-efficient LoRA, full SFT, preference tuning, and a continuous retraining loop.

Edit this fine-tuning pipeline template Back to template

LLM Fine-Tuning Pipeline Diagram Examples

Real examples

LoRA fine-tuning (parameter-efficient)

Who uses it: Developer fine-tuning on a single GPU

Base model frozen; only small adapter weights train

Dataset: a few thousand task-specific examples

Training fits on one consumer or cloud GPU

Output: a small LoRA adapter, not a full model copy

Deploy: base model + adapter loaded at serve time

Why this works: LoRA is the most accessible fine-tuning method — the diagram shows the base model frozen and only adapters training, which is why it fits on modest hardware and produces a small, swappable adapter instead of a full model.

Full supervised fine-tuning (SFT)

Who uses it: Team with budget to update all model weights

All model parameters are updated during training

Requires multi-GPU and a larger curated dataset

Checkpoints saved at intervals to the registry

Heavier eval: regression tests against base capabilities

Output: a full fine-tuned model artifact

Why this works: Full SFT updates every weight — the diagram adds checkpointing and regression eval because changing all parameters risks degrading the base model's general abilities, which must be measured before deploying.

Preference tuning (RLHF / DPO)

Who uses it: Team aligning a model to human preferences

Stage 1: SFT on demonstration data

Stage 2: collect preference pairs (chosen vs rejected)

Stage 3: DPO or RLHF optimizes against preferences

Reward model (for RLHF) or direct optimization (DPO)

Eval: win-rate against the SFT baseline

Why this works: Preference tuning adds a second training stage after SFT — the diagram shows preference data and a reward signal, because aligning to human preference is a distinct objective from imitating demonstrations.

Continuous fine-tuning

Who uses it: Team retraining as new data arrives

Production feedback feeds back into the dataset

Scheduled retraining on the growing dataset

Each run evaluated against the live model

Champion-challenger: new model must beat current

Automatic rollback if eval regresses

Why this works: Continuous fine-tuning closes the loop from production back to training — the diagram adds a feedback path and a champion-challenger gate, so a new model only replaces the live one when it measurably wins.

Tips for better study mind maps

Show the base model entering the training stage as a separate input — fine-tuning adapts an existing model, it doesn't train from scratch.
Draw the eval gate with an explicit fail path back to training; a pipeline that always deploys hasn't really evaluated.
Distinguish dataset prep from preprocessing — data collection/cleaning and tokenization are different stages with different failure modes.
Put the model registry between training and serving so deployments are versioned and rollback-able.

Start editing online

Go back to the template, swap in your own topics, and keep the same structure if it fits your class or project.

Use this template: /editor/new?template=llm-fine-tuning-pipeline

Edit this fine-tuning pipeline template