The Gemma-4 MoE per-expert Linear4bit swap previously gated only on the
positional load_in_4bit argument. loader.py forwards load_in_4bit=False
to FastBaseModel.from_pretrained whenever the caller supplies a
quantization_config (BitsAndBytesConfig), so callers that opt in via
UNSLOTH_GEMMA4_MOE_4BIT=1 plus BitsAndBytesConfig(load_in_4bit=True)
silently bypassed the swap. The adjacent guardrail already normalises
load_in_4bit from quantization_config; the swap gate now does the same
and sources bnb_4bit_compute_dtype from quantization_config when no
local bnb_config is built.
The except branch around the swap also previously stated "Falling back
to BF16 experts", which misrepresents the model state when the helper
fails partway through (already-swapped Gemma4TextExperts modules stay
in 4-bit; only the remainder remain BF16). The warning now counts the
modules marked _unsloth_gemma4_moe_4bit_swapped and reports the partial
state, advising a reload to recover a uniform state.
The comment above the fused-Parameter dels in gemma4_moe_4bit.py
overstated the swap's memory bound; rephrased to describe the actual
per-module peak (fused BF16 plus accumulated per-expert nf4).
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