Add EXAONE 4.5 implementations

convert_hf_to_gguf.py

@@ -2276,6 +2276,9 @@ def set_gguf_parameters(self):
             self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
             self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
             self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "heads", "vt_num_attention_heads"]))
+            n_kv = self.find_vparam(["num_key_value_heads"], optional=True)
+            if n_kv is not None:
+                self.gguf_writer.add_vision_head_count_kv(n_kv)
 
             # preprocessor config
             image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
@@ -11034,6 +11037,103 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Exaone4_5_ForConditionalGeneration")
+class Exaone4_5_VLTextModel(Exaone4Model):
+    """Text tower of EXAONE 4.5; Tensors match EXAONE4"""
+
+    model_arch = gguf.MODEL_ARCH.EXAONE4
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+        if n_nextn > 0:
+            self.block_count = self.hparams["num_hidden_layers"] + n_nextn
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+        if n_nextn > 0:
+            self.gguf_writer.add_nextn_predict_layers(n_nextn)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):
+            return
+        if name.startswith("model.language_model."):
+            name = name.replace("model.language_model.", "model.", 1)
+
+        if name.startswith("mtp."):
+            n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0) or 0)
+            if n_nextn <= 0:
+                return
+            nh = self.hparams["num_hidden_layers"]
+            if ".layers." in name:
+                share = self.hparams.get("mtp_share_layers", False)
+                mtp_bid = bid if bid is not None else 0
+                if share:
+                    for k in range(n_nextn):
+                        nn = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{nh + k}")
+                        yield from super().modify_tensors(data_torch, nn, nh + k)
+                    return
+                name = name.replace(f"mtp.layers.{mtp_bid}", f"model.layers.{mtp_bid + nh}")
+            else:
+                remapper = {
+                    "mtp.fc": gguf.MODEL_TENSOR.NEXTN_EH_PROJ,
+                    "mtp.pre_fc_norm_embedding": gguf.MODEL_TENSOR.NEXTN_ENORM,
+                    "mtp.pre_fc_norm_hidden": gguf.MODEL_TENSOR.NEXTN_HNORM,
+                    "mtp.norm": gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+                }
+                _n = Path(name)
+                key = _n.stem
+                if key not in remapper:
+                    return
+                for bid_mtp in range(nh, self.block_count):
+                    mapped_name = self.format_tensor_name(remapper[key], bid_mtp, suffix=_n.suffix)
+                    yield from ModelBase.modify_tensors(self, data_torch, mapped_name, bid_mtp)
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Exaone4_5_ForConditionalGeneration")
+class Exaone4_5VLVisionModel(Qwen2VLVisionModel):
+    """Vision tower for EXAONE 4.5; Qwen2-VL-style ViT (GQA) + patch merger"""
+
+    def set_gguf_parameters(self):
+        MmprojModel.set_gguf_parameters(self)
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.EXAONE4_5)
+        self.gguf_writer.add_vision_use_silu(True)
+        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+        eps = hparams.get("rms_norm_eps", self.global_config.get("rms_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_attention_layernorm_eps(eps)
+        if (window_size := hparams.get("window_size")) is not None:
+            self.gguf_writer.add_vision_window_size(window_size)
+        fullatt_block_indexes = hparams.get("fullatt_block_indexes")
+        if fullatt_block_indexes:
+            n_wa_pattern = fullatt_block_indexes[0] + 1
+            for i in range(1, len(fullatt_block_indexes)):
+                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
+                    raise ValueError(f"Invalid EXAONE4.5 fullatt_block_indexes: {fullatt_block_indexes}")
+            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.language_model.") or name.startswith("lm_head."):
+            return
+        if name.startswith("mtp."):
+            return
+        if name.startswith("model.visual."):
+            name = name.replace("model.visual.", "visual.", 1)
+
+        if name.startswith("visual.") and ".qkv." in name:
+            yield from ModelBase.modify_tensors(self, data_torch, name, bid)
+            return
+
+        yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
+
+
 @ModelBase.register("GraniteForCausalLM", "GraniteSpeechForConditionalGeneration")
 class GraniteModel(LlamaModel):
     """Conversion for IBM's GraniteForCausalLM"""
@@ -14112,7 +14212,9 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st
 
     # if "architectures" is found in the sub-config, use that instead
     if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
-        arch = text_config["architectures"][0]
+        # Multimodal EXAONE 4.5 stores the inner causal LM class in text_config; HF→GGUF must use the VL root arch.
+        if hparams.get("model_type") != "exaone4_5":
+            arch = text_config["architectures"][0]
     elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None:
         arch = vision_config["architectures"][0]
     if arch is None:

gguf-py/gguf/constants.py

@@ -466,6 +466,7 @@ class MODEL_ARCH(IntEnum):
     EXAONE           = auto()
     EXAONE4          = auto()
     EXAONE_MOE       = auto()
+    EXAONE4_5        = auto()
     GRANITE          = auto()
     GRANITE_MOE      = auto()
     GRANITE_HYBRID   = auto()
@@ -981,6 +982,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.EXAONE:           "exaone",
     MODEL_ARCH.EXAONE4:          "exaone4",
     MODEL_ARCH.EXAONE_MOE:       "exaone-moe",
+    MODEL_ARCH.EXAONE4_5:        "exaone4_5",
     MODEL_ARCH.GRANITE:          "granite",
     MODEL_ARCH.GRANITE_MOE:      "granitemoe",
     MODEL_ARCH.GRANITE_HYBRID:   "granitehybrid",
@@ -3244,6 +3246,13 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
         MODEL_TENSOR.FFN_POST_NORM,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
     MODEL_ARCH.EXAONE_MOE: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -3277,6 +3286,30 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
         MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
+    MODEL_ARCH.EXAONE4_5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_POST_NORM,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
     MODEL_ARCH.GRANITE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -4235,6 +4268,7 @@ class VisionProjectorType:
     LLAMA4 = "llama4"
     QWEN2VL = "qwen2vl_merger"
     QWEN25VL = "qwen2.5vl_merger"
+    EXAONE4_5 = "exaone4_5"
     QWEN3VL = "qwen3vl_merger"
     STEP3VL = "step3vl"
     ULTRAVOX = "ultravox"

src/models/exaone4.cpp

@@ -15,6 +15,7 @@ void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
 
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.nextn_predict_layers, false);
 
     switch (hparams.n_layer) {
         case 30: type = LLM_TYPE_1_2B; break;
@@ -38,21 +39,37 @@ void llama_model_exaone4::load_arch_tensors(llama_model_loader &) {
     }
 
     for (int i = 0; i < n_layer; ++i) {
+        const bool is_nextn = hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers;
+        int flags = 0;
+        if (is_nextn) {
+            // NextN/MTP layers are preserved in GGUF but are not executed yet.
+            flags |= TENSOR_SKIP;
+        }
+
         auto & layer = layers[i];
 
-        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
-        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, flags);
+
+        if (!is_nextn) {
+            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        }
 
-        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, flags);
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, flags);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, flags);
 
-        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
-        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
-        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, flags);
+        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, flags);
 
-        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
-        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
-        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
-        layer.ffn_post_norm  = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+        if (is_nextn) {
+            layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), {2 * n_embd, n_embd}, flags);
+            layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,   "weight", i), {n_embd}, flags);
+            layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,   "weight", i), {n_embd}, flags);
+            layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, flags | TENSOR_NOT_REQUIRED);
+        }
     }
 }
 
@@ -90,7 +107,11 @@ llama_model_exaone4::graph<iswa>::graph(const llama_model & model, const llm_gra
     }
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
-    for (int il = 0; il < n_layer; ++il) {
+    // MTP / NextN tail blocks are loaded for compatibility but not executed (same as exaone-moe).
+    const int n_layer_main = int(n_layer) - int(hparams.nextn_predict_layers);
+    GGML_ASSERT(n_layer_main > 0);
+
+    for (int il = 0; il < n_layer_main; ++il) {
         ggml_tensor * inpSA = inpL;
 
         // use RoPE for SWA layers or non-SWA models
@@ -126,7 +147,7 @@ llama_model_exaone4::graph<iswa>::graph(const llama_model & model, const llm_gra
                     Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
             cb(cur, "attn_out", il);
         }
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_layer_main - 1 && inp_out_ids) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }

tools/mtmd/CMakeLists.txt

@@ -18,6 +18,7 @@ add_library(mtmd
             models/cogvlm.cpp
             models/conformer.cpp
             models/dotsocr.cpp
+            models/exaone4_5.cpp
             models/gemma4a.cpp
             models/gemma4v.cpp
             models/glm4v.cpp

tools/mtmd/clip-impl.h

@@ -344,6 +344,7 @@ enum projector_type {
     PROJECTOR_TYPE_KIMIK25,
     PROJECTOR_TYPE_NEMOTRON_V2_VL,
     PROJECTOR_TYPE_HUNYUANOCR,
+    PROJECTOR_TYPE_EXAONE4_5,
     PROJECTOR_TYPE_HUNYUANVL,
     PROJECTOR_TYPE_MINICPMV4_6,
     PROJECTOR_TYPE_GRANITE_SPEECH,
@@ -394,6 +395,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_KIMIK25,   "kimik25"},
     { PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"},
     { PROJECTOR_TYPE_HUNYUANOCR, "hunyuanocr"},
+    { PROJECTOR_TYPE_EXAONE4_5, "exaone4_5"},
     { PROJECTOR_TYPE_HUNYUANVL,  "hunyuanvl"},
     { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"},
     { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"},