chore: import upstream snapshot with attribution

conversion/pixtral.py

@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import Sequence
+
+from .base import gguf
+
+from .llava import LlavaVisionModel
+
+
+class PixtralModel(LlavaVisionModel):
+    model_name = "Pixtral"
+    hf_arch = ""
+    is_mistral_format = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
+
+        self.gguf_writer.add_vision_attention_layernorm_eps(
+            self.find_hparam(["norm_eps"])
+        )
+        self.gguf_writer.add_rope_freq_base(self.find_vparam(["rope_theta"]))
+
+        self.gguf_writer.add_vision_use_silu(True)
+
+        # spatial_merge_size
+        if self.find_vparam(["mm_projector_id"], optional=True) == "patch_merge":
+            self.gguf_writer.add_vision_spatial_merge_size(
+                self.find_vparam(["spatial_merge_size"])
+            )
+
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
+        if name == "vision_language_adapter.w_in.weight":
+            return "mm.1.weight"
+        elif name == "vision_language_adapter.w_in.bias":
+            return "mm.1.bias"
+        elif name == "vision_language_adapter.w_out.weight":
+            return "mm.2.weight"
+        elif name == "vision_language_adapter.w_out.bias":
+            return "mm.2.bias"
+        return super().map_tensor_name(name, try_suffixes)