Training icons

The 61 training icons in AI & LLM, described

Activationactivation

An activation — nothing, nothing, then everything, the nonlinear fire of a ReLU.

Adapteradapter

An adapter — a small trained piece like LoRA bolted on to a big frozen model.

Add checkpointadd-checkpoint

A flag flown big, a plus on its banner — save a new checkpoint right now.

Alignmentalignment

Alignment — bringing a model round to what people actually want, steering with feedback.

Calibrationcalibration

Points scattered close to the identity line — how well a model's confidence matches its accuracy.

Checkpointcheckpoint

A checkpoint — a model's weights saved exactly as they were at this moment in training.

Checkpoint alertcheckpoint-alert

A flag flown big, an alert on its banner — a checkpoint that needs attention.

Checkpoint comparecheckpoint-compare

Checkpoint compare — this saved state against that one, to pick the better.

Checkpoint loadcheckpoint-load

Checkpoint load — pick up exactly where training stopped, restored from a save.

Checkpoint savecheckpoint-save

Checkpoint save — write the training state down before going on.

Datasetdataset

A dataset — the collection of examples, rows or documents a model is trained and evaluated on.

Distillationdistil

Distillation — a smaller student model taught to imitate a bigger teacher.

DPOdpo

DPO — direct preference optimisation, training a model on which of two answers people preferred.

Dropoutdropout

Dropout — some neurons sit this batch out at random to regularise the model.

Early stoppingearly-stop

Early stopping — halt training before the model starts getting worse.

Epochepoch

An epoch — one full pass over all the training data during a training run.

Eval harnesseval-harness

A play button and a check inside one frame — the harness that runs evaluations and grades them.

Find checkpointfind-checkpoint

A flag flown big, a lens on its banner — find the checkpoint you need.

Fine-tunefine-tune

Fine-tune — nudge a model with a small extra pass rather than retraining it.

Fine-tuningfine-tuning

Fine-tuning — adapting an already trained model to one domain, task or style with extra data.

Frozen checkpointfrozen-checkpoint

A flag flown big, a padlock on its banner — a checkpoint frozen so it cannot change.

Frozen layerfrozen-layer

A frozen layer — this layer keeps its weights fixed during transfer learning.

Gradient flowgrad-flow

Gradient flow — the correction travelling back down through the layers in backprop.

Gradientgradient

A gradient — the slope that says which way is downhill for the optimiser.

Gradient clipgradient-clip

Gradient clipping — no single update step gets to be bigger than the ceiling.

Gradient descentgradient-descent

Gradient descent — walk downhill on the loss until there is no downhill left.

Grokkinggrokking

A curve that stays flat and then suddenly climbs — the moment a model finally generalises.

Labelled checkpointlabelled-checkpoint

A flag flown big, a label on its banner — a checkpoint named so it can be found.

Learning ratelearning-rate

The learning rate — how big a step the optimiser takes, often decayed on a schedule.

LoRA mergelora-merge

LoRA merge — the adapter folded back into the base weights and baked in.

Lossloss

Loss — how wrong the model still is, the objective that training drives down.

Loss curveloss-curve

A loss curve — the training error coming down over time as a model converges.

LR schedulelr-schedule

An LR schedule — the learning rate stepped down on a plan as training proceeds.

Model checkpointmodel-checkpoint

A model core beside a flag — a checkpoint saved during training.

Model goalmodel-goal

A model core beside a target — the objective a model is trained toward.

Model trendmodel-trend

A model core beside a rising line — a model's quality trending over versions.

Next milestonenext-milestone

A flag flown big, a double chevron on its banner — on to the next milestone.

Noisenoise

Noise — random scatter and jitter that carries no signal worth keeping.

Noise schedulenoise-schedule

Dots shrinking step by step along a diagonal — the noise schedule of a diffusion process.

Normalisenormalize

Normalise — bring values onto the same scale so they can be compared.

Optimiseroptimiser

An optimiser — the algorithm like Adam that walks the loss downhill to a minimum.

Overfitoverfit

Overfit — a curve that followed every wobble of the training data and memorised it.

Overfit gapoverfit-gap

The overfit gap — training loss keeps falling while validation turns away.

Precision-recallprecision-recall

A curve that holds high then falls away — the precision-recall trade-off of a classifier.

Pre-trainingpretrain

Pre-training — the first and biggest pass over everything, building the base model.

Pruneprune

Prune — cut away the parameters or branches that were not earning their place.

Quantizationquantization

Quantization — using fewer bits per weight on purpose so a model runs smaller and faster.

Rank adapterrank-adapter

A rank adapter — the small LoRA piece bolted on to a big frozen model.

Regulariseregularise

Regularise — hold a model back from memorising with dropout or a penalty.

Remove checkpointremove-checkpoint

A flag flown big, a minus on its banner — delete a checkpoint you no longer need.

Resume checkpointresume-checkpoint

A flag flown big, a play on its banner — resume training from a checkpoint.

RLHFrlhf

RLHF — reinforcement learning from human feedback, a person inside the training loop.

ROC curveroc-curve

A curve bowing above the chance diagonal — the ROC curve of a classifier.

Stale checkpointstale-checkpoint

A flag flown big, a Z on its banner — a checkpoint too old to be useful any more.

Patiencestop-patience

Patience — the plateau is on the clock before early stopping ends the run.

Training losstrain-loss

Training loss — down, then flat, the curve every run hopes to draw.

Trainingtraining

Training — teaching a model from data by adjusting its weights over many epochs.

Training datatraining-data

Training data — the labelled examples and corpus a model learned from.

Underfitunderfit

Underfit — a line too simple for the shape it is chasing, high bias that misses the pattern.

Underfitunderfit-gap

Underfit — both curves stay high, the model has not learned enough yet.

Warmupwarmup

Warmup — the learning rate ramps gently up to speed, then holds.

In code, each is one import — import { Activation } from "@iconmind/react/icons/activation" — and the same name in Vue, Svelte, Solid, Preact, React Native, Astro, Blade and Flutter.

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