from __future__ import annotations
from typing import Callable, Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, gguf, logger
@ModelBase.register("YoutuVLForConditionalGeneration")
@ModelBase.example("tencent/Youtu-VL-4B-Instruct")
class YoutuVLVisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
# Handle activation function
hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
self.gguf_writer.add_vision_use_gelu(True)
elif hidden_act == "silu":
self.gguf_writer.add_vision_use_silu(True)
else:
raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
window_size = self.hparams.get("window_size")
if window_size is not None:
self.gguf_writer.add_vision_window_size(window_size)
# fullatt_block_indexes contains explicit layer indices that use full attention
# e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
# All other layers use window attention
fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
# Store the explicit layer indices for YoutuVL (irregular pattern approach)
self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# Skip language model tensors
skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
if name.startswith(skip_prefixes):
return None
return super().filter_tensors(item)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# Try to map the tensor using TensorNameMap (handles vision encoder and projector)
try:
yield from super().modify_tensors(data_torch, name, bid)
except ValueError:
# If mapping fails, log warning and skip
logger.warning(f"Cannot map tensor: {name}")
return