def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        assert self.has_vision_encoder
        assert self.hparams_vision is not None
  
        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
    def set_gguf_parameters(self):
        # remain code from ddh0 as is
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid # unused
        if name.startswith("model.visual."):
            name = name.replace("model.visual.", "visual.", 1)

            if ".qkv." in name:
                if data_torch.ndim == 2: # weight
                    c3, _ = data_torch.shape
                else: # bias
                    c3 = data_torch.shape[0]
                assert c3 % 3 == 0
                c = c3 // 3
                wq = data_torch[:c]
                wk = data_torch[c: c * 2]
                wv = data_torch[c * 2:]
                return [
                    (self.map_tensor_name(name.replace("qkv", "q")), wq),
                    (self.map_tensor_name(name.replace("qkv", "k")), wk),
                    (self.map_tensor_name(name.replace("qkv", "v")), wv),
                ]

            if name.startswith("visual.downsample."):
                suffix = name.split(".", 2)[2]
                new_name = self.format_tensor_name(gguf.MODEL_TENSOR.V_POST_NORM, suffix=suffix[1])
                return [(new_name, data_torch)]

            yield self.map_tensor_name(name), data_torch
        else:
            return