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llama.cpp
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refactor: simplify sliding window pattern handling in VaetkiModel

This commit is contained in:
suhyun-hwang 2026-01-15 00:05:42 +09:00
parent 5d0870207a
commit 75323b3e08
1 changed files with 5 additions and 13 deletions
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18
convert_hf_to_gguf.py
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@ -7748,19 +7748,11 @@ class VaetkiModel(TextModel):
if hparams.get("norm_topk_prob", False):
self.gguf_writer.add_expert_weights_norm(True)
# Sliding window and hybrid attention pattern
if "sliding_window" in hparams:
self.gguf_writer.add_sliding_window(hparams["sliding_window"])
# Add sliding window pattern from layer_types
if "layer_types" in hparams:
# Convert layer_types to sliding_window_pattern (1 = sliding, 0 = full)
# Store as uint32 array to match llama.cpp hparams.swa_layers type
sliding_window_pattern = [1 if t == "sliding_attention" else 0 for t in hparams["layer_types"]]
self.gguf_writer.add_array(
gguf.Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.gguf_writer.arch),
sliding_window_pattern
)
self.gguf_writer.add_sliding_window(hparams["sliding_window"])
sliding_window_pattern = []
for t in self.hparams["layer_types"]:
sliding_window_pattern.append(int(t == "sliding_attention"))
self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Skip vision encoder tensors