chore: import upstream snapshot with attribution

conversion/mistral3.py

@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+from .deepseek import DeepseekV2Model
+from .llama import LlamaModel
+
+
+@ModelBase.register(
+    "Mistral3ForConditionalGeneration",
+    "Ministral3ForCausalLM",
+)
+class Mistral3Model(TextModel):
+    class Ministral3Model(LlamaModel):
+        model_arch = gguf.MODEL_ARCH.MISTRAL3
+
+        def set_gguf_parameters(self):
+            super().set_gguf_parameters()
+            rope_params = self.rope_parameters
+            if self.hparams.get("model_type") == "ministral3":
+                assert rope_params, "ministral3 must have 'rope_parameters' config"
+                assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
+                self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
+                self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
+
+    class Mistral4Model(DeepseekV2Model):
+        model_arch = gguf.MODEL_ARCH.MISTRAL4
+        skip_mtp = False # model contains no MTP layers, so no need to skip
+        merge_expert = False # experts are already stacked as 3D
+
+        def modify_tensors(self, data_torch, name, bid):
+            if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
+                name = name + ".weight"
+            yield from super().modify_tensors(data_torch, name, bid)
+
+    model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
+    impl: TextModel
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams.get("model_type") == "mistral4":
+            self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
+        else:
+            self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)
+
+    def set_vocab(self):
+        self.impl.set_vocab()
+
+    def set_gguf_parameters(self):
+        self.impl.set_gguf_parameters()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        yield from self.impl.modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        self.impl.prepare_tensors()
+
+    def write_vocab(self):
+        self.impl.write_vocab()
+
+    def write(self):
+        self.impl.write()