chore: import upstream snapshot with attribution

conversion/sarashina2.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing import Callable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, gguf
+
+from .llama import LlamaModel
+from .qwenvl import Qwen2VLVisionModel
+
+
+@ModelBase.register("Sarashina2VisionForCausalLM")
+class Sarashina2VLTextModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith("llm."):
+            name = name.replace("llm.", "", 1)
+        elif name.startswith("norm."):
+            return None
+        return super().filter_tensors((name, gen))
+
+
+@ModelBase.register("Sarashina2VisionForCausalLM")
+class Sarashina2VLVisionModel(Qwen2VLVisionModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.global_config['model_type'] = "qwen2_vl"