address review comments

conversion/dots3.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import math
+import re
 
 from typing import TYPE_CHECKING, Callable, Iterable
 
@@ -16,6 +17,14 @@ from .deepseek import DeepseekV2Model
 class Dots3NoteModel(DeepseekV2Model):
     model_arch = gguf.MODEL_ARCH.DOTS3NOTE
     skip_mtp = False
+    supports_mtp_export = True
+
+    # trunk layer count, stashed before indexing for filter_tensors (mirrors DeepseekV32Model)
+    _n_main_layers: int | None = None
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -75,10 +84,25 @@ class Dots3NoteModel(DeepseekV2Model):
 
     @classmethod
     def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, _ = item
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
         if name.startswith(("vision_encoder.", "audio_encoder.")):
             return None
-        return super().filter_tensors(item)
+
+        assert cls._n_main_layers is not None
+        is_mtp = name.startswith("model.mtp.") or \
+            ((m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers)
+
+        # --no-mtp: drop the NextN/MTP block; --mtp: keep only that block plus the shared embeddings/norm/lm_head
+        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 set_gguf_parameters(self):
         hparams = self.hparams
@@ -116,6 +140,19 @@ class Dots3NoteModel(DeepseekV2Model):
         self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
         self.gguf_writer.add_indexer_types([not self._is_swa_layer(il) for il in range(n_layer)])
 
+    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]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # move the MTP token embedding into the NextN block so the standard nextn mapping picks it up
         if name == "model.mtp.embed_tokens.weight":

src/llama-model.cpp

@@ -847,6 +847,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
         case LLM_TYPE_428B_A23B:     return "428B.A23B";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
+        case LLM_TYPE_288B_A19B:     return "288B.A19B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
         case LLM_TYPE_310B_A15B:     return "310B.A15B";
         case LLM_TYPE_355B_A32B:     return "355B.A32B";

src/llama-model.h

@@ -140,6 +140,7 @@ enum llm_type {
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_428B_A23B, // Minimax M3
     LLM_TYPE_235B_A22B,
+    LLM_TYPE_288B_A19B, // dots3-note
     LLM_TYPE_300B_A47B, // Ernie MoE big
     LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
     LLM_TYPE_355B_A32B, // GLM-4.5

src/models/dots3note.cpp

@@ -37,19 +37,16 @@ void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) {
     hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
     ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA,       hparams.rope_freq_base_train_swa);
-    ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+    ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
 
-    // DSA parameters - by default the indexer exists exactly on the full-attention layers
+    // DSA parameters
     ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
     ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
     ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);
-    for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
-        hparams.is_indexer_full_impl[il] = hparams.is_swa(il) ? 0 : 1;
-    }
-    ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
+    ml.get_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl);
 
     switch (hparams.n_layer()) {
-        case 46: type = LLM_TYPE_UNKNOWN; break; // 288B-A19B
+        case 46: type = LLM_TYPE_288B_A19B; break;
         default: type = LLM_TYPE_UNKNOWN;
     }
 }

tests/test-llama-archs.cpp

@@ -185,6 +185,13 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
             ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA,   uint32_t(192));
             ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, uint32_t(128));
             ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,             10000.0f);
+            // indexer on the full-attention layers (inverse of the swa pattern)
+            std::vector<uint32_t> indexer_types;
+            indexer_types.reserve(n_layer);
+            for (uint32_t il = 0; il < n_layer; il++) {
+                indexer_types.push_back(il % 2 ? 0 : 1);
+            }
+            ms.add_kv(LLM_KV_ATTENTION_INDEXER_TYPES, indexer_types);
         }
     } else if (arch == LLM_ARCH_MINIMAX_M3) {
         // partial rotary: n_rot must not exceed the indexer key length (64)