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Map to symbolic V_MMPROJ instead of strings

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Pedro Cuenca
2026-08-06 12:43:04 +02:00
parent d6e2e02d75
commit 06a8f4eee7
1 changed files with 8 additions and 6 deletions
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conversion/onyx.py
+8 -6
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@@ -112,18 +112,20 @@ class OnyxVisionModel(MmprojModel):
return tensor.view(n_heads, 2, dim1 // n_heads // 2).transpose(1, 2).reshape(dim1)
raise ValueError(f"_unpermute_for_rope: unexpected shape {tuple(tensor.shape)}")
# 3-layer projector MLP: emit as the standard V_MMPROJ numbered slots (mm.0/mm.1/mm.2)
# 3-layer projector MLP
_MM_MLP_MAP = {
"model.vision_adapter.fc1.weight": "mm.0.weight",
"model.vision_adapter.fc2.weight": "mm.1.weight",
"model.vision_projection.weight": "mm.2.weight",
"model.vision_adapter.fc1": (gguf.MODEL_TENSOR.V_MMPROJ, 0),
"model.vision_adapter.fc2": (gguf.MODEL_TENSOR.V_MMPROJ, 1),
"model.vision_projection": (gguf.MODEL_TENSOR.V_MMPROJ, 2),
}
def modify_tensors(self, data_torch, name, bid):
if ".attn.q_proj." in name or ".attn.k_proj." in name:
n_heads = int(self.hparams_vision["num_attention_heads"])
data_torch = self._unpermute_for_rope(data_torch, n_heads)
if name in self._MM_MLP_MAP:
yield (self._MM_MLP_MAP[name], data_torch)
stem, _, suffix = name.rpartition(".")
if stem in self._MM_MLP_MAP:
tensor_key, idx = self._MM_MLP_MAP[stem]
yield (self.format_tensor_name(tensor_key, bid=idx, suffix="." + suffix), data_torch)
return
yield (self.map_tensor_name(name), data_torch)