jdelony/llama.cpp
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convert : minor fixes for numpy 2.x (#23571)

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Sigbjørn Skjæret 2026-05-24 09:51:31 +02:00 committed by GitHub
parent 63248fc3e3
commit 5d246a792d
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2 changed files with 3 additions and 1 deletions
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3
examples/convert_legacy_llama.py
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@ -1308,7 +1308,8 @@ def do_dump_model(model_plus: ModelPlus) -> None:
def main(args_in: list[str] | None = None) -> None: def main(args_in: list[str] | None = None) -> None:
output_choices = ["f32", "f16"] output_choices = ["f32", "f16"]
if np.uint32(1) == np.uint32(1).newbyteorder("<"): dummy_val = np.uint32(1)
if dummy_val == dummy_val.view(dummy_val.dtype.newbyteorder("<")):
# We currently only support Q8_0 output on little endian systems. # We currently only support Q8_0 output on little endian systems.
output_choices.append("q8_0") output_choices.append("q8_0")
parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file") parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")

1
gguf-py/gguf/quants.py
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@ -28,6 +28,7 @@ def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizati
# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time # This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time
def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray: def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray:
rows = arr.reshape((-1, arr.shape[-1])) rows = arr.reshape((-1, arr.shape[-1]))
assert len(rows.shape)
osize = 1 osize = 1
for dim in oshape: for dim in oshape:
osize *= dim osize *= dim