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39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
from __future__ import annotations
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from typing import Callable, Iterable, TYPE_CHECKING
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import MmprojModel, ModelBase, gguf
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@ModelBase.register("Llama4ForConditionalGeneration")
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class Llama4VisionModel(MmprojModel):
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4)
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self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
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self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
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assert self.hparams["hidden_act"] == "gelu"
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self.gguf_writer.add_vision_use_gelu(True)
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@classmethod
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def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
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name, gen = item
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if "multi_modal_projector" not in name and "vision_model" not in name:
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return None
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if "positional_embedding_vlm" in name and ".weight" not in name:
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name += ".weight"
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return super().filter_tensors((name, gen))
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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if "multi_modal_projector.linear_1" in name:
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# despite the name with number postfix, this is a single fully connected layer
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yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch)
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else:
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yield from super().modify_tensors(data_torch, name, bid)
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