tmp

Hoist the K3 mxfp4 conversion repack into base.py so it can be reused
Remove decoder from mmproj convert

conversion/base.py

@@ -776,6 +776,36 @@ class ModelBase:
         raw = torch.cat((s.unsqueeze(-1), qs.to(torch.uint8)), dim=-1)
         return raw.reshape(rows, n_blocks * 17).cpu().numpy()
 
+    def _mxfp4_expert_tensor(self, loaders: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]):
+        """
+        One stacked [n_expert, rows, cols] MXFP4 tensor, built lazily.
+
+        gguf_writer holds every added tensor until the final write, so building
+        this eagerly (like the DeepSeek-V4 path does) keeps every expert in
+        memory at once. lazy means only the tensor being written is resident.
+        """
+        # meta shapes, so this does not read any weights
+        rows, packed_cols = loaders[0][0]().shape
+        n_blocks = (packed_cols * 2) // 32
+        byte_shape = (len(loaders), rows, n_blocks * 17)
+
+        def load(fns: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]) -> np.ndarray:
+            out = np.empty(byte_shape, dtype=np.uint8)
+            for eid, (packed_fn, scale_fn) in enumerate(fns):
+                out[eid] = self.repack_mxfp4_blocks(
+                    LazyTorchTensor.to_eager(packed_fn()),
+                    LazyTorchTensor.to_eager(scale_fn()),
+                )
+            return out
+
+        # loaders goes through args, not the closure, so that `func` matches
+        # LazyBase's single-argument shape
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(np.uint8, byte_shape),
+            args=(loaders,),
+            func=load,
+        )
+
     @staticmethod
     def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
         """Repack NVFP4 ModelOpt tensors into ggml super-block layout.

conversion/kimi_k3.py

@@ -2,15 +2,14 @@ from __future__ import annotations
 
 import re
 from pathlib import Path
-from typing import Callable, Iterable, Iterator, TYPE_CHECKING
+from typing import Iterable, Iterator, TYPE_CHECKING
 
-import numpy as np
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import LazyTorchTensor, ModelBase, TextModel, gguf, logger
+from .base import ModelBase, TextModel, gguf, logger
 
 from .kimi_linear import KimiLinearModel
 
@@ -104,36 +103,6 @@ class KimiK3Model(TextModel):
                 "only the routed experts have a repack path"
             )
 
-    def _mxfp4_expert_tensor(self, loaders: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]):
-        """
-        One stacked [n_expert, rows, cols] MXFP4 tensor, built lazily.
-
-        gguf_writer holds every added tensor until the final write, so building
-        this eagerly (like the DeepSeek-V4 path does) keeps all ~1.38 TB of
-        experts in memory. lazy means only the tensor being written is resident.
-        """
-        # meta shapes, so this does not read any weights
-        rows, packed_cols = loaders[0][0]().shape
-        n_blocks = (packed_cols * 2) // 32
-        byte_shape = (len(loaders), rows, n_blocks * 17)
-
-        def load(fns: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]) -> np.ndarray:
-            out = np.empty(byte_shape, dtype=np.uint8)
-            for eid, (packed_fn, scale_fn) in enumerate(fns):
-                out[eid] = self.repack_mxfp4_blocks(
-                    LazyTorchTensor.to_eager(packed_fn()),
-                    LazyTorchTensor.to_eager(scale_fn()),
-                )
-            return out
-
-        # loaders goes through args, not the closure, so that `func` matches
-        # LazyBase's single-argument shape
-        return gguf.LazyNumpyTensor(
-            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(np.uint8, byte_shape),
-            args=(loaders,),
-            func=load,
-        )
-
     def _write_mxfp4_experts(self) -> None:
         n_experts = self.hparams["num_experts"]
 

conversion/mimo.py

@@ -10,13 +10,14 @@ import torch
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import MmprojModel, ModelBase, TextModel, gguf
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger
 
 
 @ModelBase.register("MiMoV2FlashForCausalLM", "MiMoV2ForCausalLM")
 @ModelBase.example("XiaomiMiMo/MiMo-V2.5")
 class MimoV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.MIMO2
+    supports_mtp_export = True
 
     # MiMo V2-Flash, V2.5 and V2.5-Pro all ship 3 trained MTP layers under model.mtp.layers.{0,1,2}.
     # The HF config does not expose the count, so it's hardcoded to match the count found in the safetensors.
@@ -25,6 +26,9 @@ class MimoV2Model(TextModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
+        if self.no_mtp:
+            return
+
         self.block_count = self.hparams["num_hidden_layers"] + self._n_nextn
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
@@ -146,7 +150,7 @@ class MimoV2Model(TextModel):
         n_head_kv_swa = self.hparams["swa_num_key_value_heads"]
         # Extend the per-layer pattern with SWA entries for the MTP blocks so the
         # runtime arrays (sized to extended block_count) are fully populated.
-        hybrid = list(self.hparams["hybrid_layer_pattern"]) + [1] * self._n_nextn
+        hybrid = list(self.hparams["hybrid_layer_pattern"]) + ([] if self.no_mtp else [1] * self._n_nextn)
         n_head_kv_arr = [n_head_kv_swa if use_swa == 1 else n_head_kv for use_swa in hybrid]
         self.gguf_writer.add_head_count_kv(n_head_kv_arr)
 
@@ -165,7 +169,97 @@ class MimoV2Model(TextModel):
         if v_scale is not None:
             self.gguf_writer.add_attn_value_scale(float(v_scale))
 
-        self.gguf_writer.add_nextn_predict_layers(self._n_nextn)
+        if not self.no_mtp:
+            self.gguf_writer.add_nextn_predict_layers(self._n_nextn)
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,                  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)    # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
+    _MXFP4_EXPERT_RE = re.compile(
+        r"^model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate|up|down)_proj\.weight$"
+    )
+    _MXFP4_PROJ = {
+        "gate": gguf.MODEL_TENSOR.FFN_GATE_EXP,
+        "up":   gguf.MODEL_TENSOR.FFN_UP_EXP,
+        "down": gguf.MODEL_TENSOR.FFN_DOWN_EXP,
+    }
+
+    def _is_mxfp4_packed(self) -> bool:
+        quant_config = self.hparams.get("quantization_config") or {}
+        if quant_config.get("store_dtype") != "mxfp4":
+            return False
+        # repack_mxfp4_blocks assumes ggml's 32-element group
+        block_size = quant_config.get("mxfp4_block_size", 32)
+        if block_size != 32:
+            raise NotImplementedError(
+                f"MXFP4 block size {block_size} is not ggml's QK_MXFP4 (32)")
+        return True
+
+    def _write_mxfp4_experts(self) -> None:
+        n_experts = self.hparams["n_routed_experts"]
+
+        # the FP8 half uses `weight_scale_inv` and is left to dequant_model
+        stray = [n for n in self.model_tensors
+                 if n.endswith(".weight_scale") and not self._MXFP4_EXPERT_RE.match(n.removesuffix("_scale"))]
+        if stray:
+            raise NotImplementedError(
+                f"{len(stray)} MXFP4 tensor(s) outside the routed experts, e.g. {stray[0]!r}; "
+                "only the routed experts have a repack path"
+            )
+
+        # (bid, proj) -> {expert id: (weight name, scale name)}
+        groups: dict[tuple[int, str], dict[int, tuple[str, str]]] = {}
+        for name in self.model_tensors:
+            m = self._MXFP4_EXPERT_RE.match(name)
+            if m is None:
+                continue
+            bid, eid, proj = int(m.group(1)), int(m.group(2)), m.group(3)
+            scale_name = name + "_scale"
+            if scale_name not in self.model_tensors:
+                raise KeyError(f"missing {scale_name} for {name}")
+            groups.setdefault((bid, proj), {})[eid] = (name, scale_name)
+
+        consumed: list[str] = []
+        for (bid, proj), experts in sorted(groups.items()):
+            missing = [e for e in range(n_experts) if e not in experts]
+            if missing or len(experts) != n_experts:
+                raise KeyError(
+                    f"layer {bid} {proj}_proj: {len(experts)} of {n_experts} experts present"
+                    + (f", first missing is {missing[0]}" if missing else "")
+                )
+
+            loaders = []
+            for eid in range(n_experts):
+                weight_name, scale_name = experts[eid]
+                loaders.append((self.model_tensors[weight_name], self.model_tensors[scale_name]))
+                consumed += [weight_name, scale_name]
+
+            data = self._mxfp4_expert_tensor(loaders)
+            new_name = self.format_tensor_name(self._MXFP4_PROJ[proj], bid)
+            shape = gguf.quant_shape_from_byte_shape(data.shape, gguf.GGMLQuantizationType.MXFP4)
+            logger.info(
+                f"{new_name}: repacked {n_experts} experts to MXFP4, "
+                f"shape = {{{', '.join(str(n) for n in reversed(shape))}}}"
+            )
+            self.gguf_writer.add_tensor(new_name, data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+        for name in consumed:
+            del self.model_tensors[name]
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self._is_mxfp4_packed():
+            self._write_mxfp4_experts()
+        return ()
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -176,7 +270,20 @@ class MimoV2Model(TextModel):
         if "attention_sink" in name and not name.endswith(".weight"):
             name += ".weight"
 
-        return super().filter_tensors((name, gen))
+        if (titem := super().filter_tensors((name, gen))) is None:
+            return None
+        name, gen = titem
+
+        is_mtp = (m := re.match(r"^model\.mtp\.layers\.", name)) is not None
+
+        if is_mtp and cls.no_mtp:
+            return None
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
+        return name, gen
 
     def modify_tensors(self, data_torch, name, bid):
         # Remap MTP/NextN tensors to additional layer slots so the standard tensor map handles them.
@@ -192,7 +299,7 @@ class MimoV2Model(TextModel):
             bid = new_bid
 
         # process the experts separately
-        if name.find("mlp.experts") != -1:
+        if ".mlp.experts." in name and name.endswith(".weight"):
             n_experts = self.hparams["n_routed_experts"]
             assert bid is not None
 
@@ -229,6 +336,10 @@ class MimoV2Model(TextModel):
             if len(experts) > 0:
                 raise ValueError(f"Unprocessed experts: {experts}")
 
+        if self._is_mxfp4_packed():
+            self._is_mxfp4 = True
+            self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+
 
 @ModelBase.register("MiMoV2ForCausalLM")
 @ModelBase.example("XiaomiMiMo/MiMo-V2.5")
@@ -382,6 +493,8 @@ class MiMoV2VisionAudioModel(MmprojModel):
             "_codebook.inited",
         )
         for name, tensor in state_dict.items():
+            if name.startswith("decoder."):
+                continue
             if name.endswith(skip_suffixes):
                 continue
             if m := codebook_re.match(name):