| from __future__ import annotations |
|
|
| from typing import Callable, Iterable, TYPE_CHECKING |
|
|
| if TYPE_CHECKING: |
| from torch import Tensor |
|
|
| from .base import MmprojModel, ModelBase, gguf |
|
|
|
|
| @ModelBase.register("Llama4ForConditionalGeneration") |
| |
| @ModelBase.example("unsloth/Llama-4-Scout-17B-16E-Instruct") |
| class Llama4VisionModel(MmprojModel): |
| def set_gguf_parameters(self): |
| super().set_gguf_parameters() |
| self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4) |
| self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"]) |
| self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"])) |
| assert self.hparams["hidden_act"] == "gelu" |
| self.gguf_writer.add_vision_use_gelu(True) |
|
|
| @classmethod |
| def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None: |
| name, gen = item |
|
|
| if "multi_modal_projector" not in name and "vision_model" not in name: |
| return None |
|
|
| if "positional_embedding_vlm" in name and ".weight" not in name: |
| name += ".weight" |
|
|
| return super().filter_tensors((name, gen)) |
|
|
| def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: |
| if "multi_modal_projector.linear_1" in name: |
| |
| yield (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC] + '.weight', data_torch) |
| else: |
| yield from super().modify_tensors(data_torch, name, bid) |
|
|
