interlens.interp.layers¶

def decoder_layers(model: "PreTrainedModel"):
	"""Return the list of transformer decoder layer modules, across common HF architectures.

	Steering/patching hooks and per-layer capture all need the ordered layer stack. Qwen and Gemma both expose
	it at ``model.model.layers``; a few fallbacks cover other families so the interp layer isn't Qwen/Gemma-only.
	Multimodal image-text-to-text wrappers (Qwen 3.5, Gemma 4, …) nest the text decoder one level deeper — its
	layers live at ``model.language_model.layers`` (or ``.model.language_model.model.layers``) — so those paths are
	included too. PEFT/adapter wrappers (``PeftModelForCausalLM``) are unwrapped first so hooks land on the real
	decoder layers.
	"""
	if hasattr(model, "get_base_model"):  # peft wrapper -> underlying transformers model
		try:
			model = model.get_base_model()
		except Exception:
			pass
	for path in ("model.layers", "transformer.h", "gpt_neox.layers", "model.decoder.layers",
	             "model.language_model.layers", "language_model.layers", "model.language_model.model.layers"):
		obj = model
		try:
			for attr in path.split("."):
				obj = getattr(obj, attr)
			return obj
		except AttributeError:
			continue
	raise AttributeError(f"could not locate decoder layers on {type(model).__name__}")