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BitsAndBytes 4-bit quantization packs weights as uint8 with shape [total_elements//2, 1], which breaks the existing weight.shape-based dimension detection in SwitchedLoRALinear.__init__(). Fix: - Prefer input_size_per_partition / output_size_per_partition attributes (always correct, regardless of weight packing format) - Fall back to weight.shape only for non-parallel layers - Add dtype guard: if weight dtype is non-floating-point (uint8 for BnB), default to bfloat16 for LoRA buffer allocation Also adds vLLM quantization tests (BnB INT4 + FP8) that verify: - Base model weights are actually quantized - LoRA/aLoRA weights remain in full precision - Adapters activate correctly under quantization - LoRA dimensions are not corrupted by packed weight shapes Closes #16
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BitsAndBytes 4-bit quantization packs weights as uint8 with shape [total_elements//2, 1], which breaks the existing weight.shape-based dimension detection in SwitchedLoRALinear.init().
Fix:
Also adds vLLM quantization tests (BnB INT4 + FP8) that verify: