diff --git a/conversion/__init__.py b/conversion/__init__.py
index ba73192efa1..94d6a49fbc9 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -124,6 +124,7 @@
     "HunYuanMoEV1ForCausalLM": "hunyuan",
     "HunYuanVLForConditionalGeneration": "hunyuan",
     "HYV3ForCausalLM": "hunyuan",
+    "HYV4ForCausalLM": "hy_v4",
     "IQuestCoderForCausalLM": "llama",
     "InternLM2ForCausalLM": "internlm",
     "InternLM3ForCausalLM": "internlm",
diff --git a/conversion/base.py b/conversion/base.py
index daae28e92ad..c1ecf1c651b 100644
--- a/conversion/base.py
+++ b/conversion/base.py
@@ -1507,6 +1507,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
             # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
             res = "hunyuan-dense"
+        if chkhsh == "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c":
+            # ref: https://huggingface.co/tencent/Hy4-preview
+            res = "hy_v4"
         if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
             # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
             res = "falcon-h1"
diff --git a/conversion/hy_v4.py b/conversion/hy_v4.py
new file mode 100644
index 00000000000..f564b9ec25f
--- /dev/null
+++ b/conversion/hy_v4.py
@@ -0,0 +1,311 @@
+from __future__ import annotations
+
+import re
+from typing import Iterable
+
+import torch
+
+from .base import ModelBase, gguf, logger
+from .deepseek import DeepseekV2Model
+
+
+def split_kv_b_proj(weight: torch.Tensor, n_head: int, qk_nope: int, v_head_dim: int):
+    """Split kv_b_proj into k_b (transposed) and v_b, matching DeepSeek MLA absorption.
+
+    weight: [n_head*(qk_nope+v_head_dim), kv_lora_rank].
+    Returns (k_b, v_b): k_b [n_head, kv_lora_rank, qk_nope], v_b [n_head, v_head_dim, kv_lora_rank].
+    """
+    kv_lora = weight.shape[-1]
+    assert weight.shape[0] == n_head * (qk_nope + v_head_dim)
+    kv_b = weight.view(n_head, qk_nope + v_head_dim, kv_lora)
+    k_b, v_b = torch.split(kv_b, [qk_nope, v_head_dim], dim=1)
+    k_b = k_b.transpose(1, 2).contiguous()  # [n_head, kv_lora, qk_nope]
+    return k_b, v_b.contiguous()
+
+
+def split_gate_up(weight: torch.Tensor, moe_intermediate_size: int):
+    """Split a fused stacked gate_up expert tensor into (gate, up).
+
+    weight: [n_expert, 2*moe_intermediate_size, hidden] (gate first, up second).
+    Returns (gate, up) each [n_expert, moe_intermediate_size, hidden].
+    """
+    assert weight.shape[1] == 2 * moe_intermediate_size, f"{weight.shape[1]} != 2*{moe_intermediate_size}"
+    gate = weight[:, :moe_intermediate_size, :].contiguous()
+    up = weight[:, moe_intermediate_size:, :].contiguous()
+    return gate, up
+
+
+@ModelBase.register("HYV4ForCausalLM")
+class HYV4Model(DeepseekV2Model):
+    """HY_V4: DeepSeek-V3 style MLA + MoE with iHC, a gated MLA output and a learnable sink.
+
+    Reuses DeepseekV2Model for the vocab and the MLA metadata, but overrides the tensor mapping
+    because HY_V4 ships pre-stacked / fused experts plus extra iHC, gate and sink tensors. The
+    rope rows are mapped straight through (no permute) - the graph rotates consecutive pairs.
+
+    DSA is supported: indexer weights are exported for the layers marked "full" in indexer_types.
+    "shared" layers reuse the top-k of the last preceding full layer at inference time, so they
+    carry no indexer weights.
+
+    MTP (num_nextn_predict_layers) is dropped, so the GGUF cannot be used for speculative
+    decoding. The reference only runs the MTP layers while training or while speculating, so they
+    cannot change single-token logits.
+    """
+
+    model_arch = gguf.MODEL_ARCH.HY_V4
+
+    # tensors a "full" indexer layer must carry
+    INDEXER_SUFFIXES = frozenset({
+        "self_attn.indexer.wq_b.weight",
+        "self_attn.indexer.wk.weight",
+        "self_attn.indexer.k_norm.weight",
+        "self_attn.indexer.k_norm.bias",
+        "self_attn.indexer.weights_proj.weight",
+    })
+
+    @classmethod
+    def filter_tensors(cls, item):
+        # drop MTP here, not in modify_tensors, so the weights are never read
+        if item[0].startswith("model.mtp_layers."):
+            return None
+        return super().filter_tensors(item)
+
+    def _check_indexer_hparams(self):
+        for key in ("index_n_heads", "index_head_dim", "index_topk"):
+            if key not in self.hparams:
+                raise ValueError(f"HY_V4 has DSA layers but no {key}")
+
+    def indexer_is_full(self) -> list[bool] | None:
+        """Per-layer indexer ownership, or None when the checkpoint has no DSA.
+
+        indexer_types entries are "full" (owns an indexer) or "shared" (reuses the preceding
+        full layer's top-k). Missing indexer_types with sparse layers means every sparse layer
+        owns one.
+        """
+        hparams = self.hparams
+        n_layer = hparams["num_hidden_layers"]
+        indexer_types = hparams.get("indexer_types")
+
+        # the reference drives DSA off indexer_types alone; layer_types is only a fallback for
+        # checkpoints predating it (it was renamed to deepseek_sparse_attention upstream)
+        if indexer_types is None:
+            layer_types = hparams.get("layer_types") or []
+            sparse = {"sparse_attention", "deepseek_sparse_attention"}
+            if not any(t in sparse for t in layer_types):
+                return None
+            if len(layer_types) < n_layer:
+                raise ValueError(f"HY_V4 layer_types has {len(layer_types)} entries, need {n_layer}")
+            self._check_indexer_hparams()
+            return [t in sparse for t in layer_types[:n_layer]]
+
+        self._check_indexer_hparams()
+
+        if len(indexer_types) < n_layer:
+            raise ValueError(f"HY_V4 indexer_types has {len(indexer_types)} entries, need {n_layer}")
+        unknown = {t for t in indexer_types[:n_layer]} - {"full", "shared"}
+        if unknown:
+            raise ValueError(f"HY_V4 unknown indexer_types values: {sorted(unknown)}")
+        is_full = [t == "full" for t in indexer_types[:n_layer]]
+        if is_full and not is_full[0]:
+            raise ValueError("HY_V4 layer 0 must be indexer_types 'full' (nothing precedes it to share)")
+        return is_full
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+
+        # HY4 has n_group == topk_group == 1 (no group routing). Drop the keys so the base does
+        # not emit expert_group_count/used; llama.cpp then takes the ungrouped MoE path.
+        if hparams.get("n_group") == 1 and hparams.get("topk_group") == 1:
+            hparams.pop("n_group", None)
+            hparams.pop("topk_group", None)
+
+        # HY_V4 config expresses dense/sparse layers via mlp_layer_types, but DeepseekV2Model
+        # needs first_k_dense_replace. Derive it as the contiguous leading "dense" block
+        # (the real config.json also carries first_k_dense_replace; prefer it when present,
+        # but assert the two agree so a mismatch fails loudly).
+        mlp_types = hparams.get("mlp_layer_types")
+        explicit = hparams.get("first_k_dense_replace")
+        derived = None
+        if mlp_types is not None:
+            lead = 0
+            for t in mlp_types:
+                if t == "dense":
+                    lead += 1
+                else:
+                    break
+            if any(t == "dense" for t in mlp_types[lead:]):
+                raise NotImplementedError("HY_V4 converter expects a contiguous leading dense block")
+            derived = lead
+        if explicit is not None and derived is not None and explicit != derived:
+            raise ValueError(
+                f"HY_V4 first_k_dense_replace ({explicit}) disagrees with mlp_layer_types "
+                f"leading-dense count ({derived})"
+            )
+        if explicit is None:
+            if derived is None:
+                raise ValueError("HY_V4 needs first_k_dense_replace or mlp_layer_types to place dense layers")
+            hparams["first_k_dense_replace"] = derived
+
+        # reuse DeepseekV2 MLA + MoE metadata (forces num_key_value_heads=1, writes q/kv lora,
+        # key/value lengths, expert counts, weights scale/norm, rope dims, etc.)
+        super().set_gguf_parameters()
+
+        # HY4 uses DeepSeek-V3 sigmoid routing with e_score_correction_bias. The config has no
+        # scoring_func key, so the base does not write a gating func; set it explicitly.
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # routed-expert SwiGLU logits clamp (only routed experts; shared/dense are not clamped,
+        # so swiglu_clamp_shexp is intentionally not written). 0.0 disables the clamp.
+        swiglu_limit = float(hparams.get("swiglu_limit", 0.0) or 0.0)
+        if swiglu_limit > 0.0:
+            self.gguf_writer.add_swiglu_clamp_exp([swiglu_limit] * self.block_count)
+
+        # iHC (independent Hyper-Connections)
+        self.gguf_writer.add_hyper_connection_count(hparams["hc_mult"])
+        self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
+        self.gguf_writer.add_hyper_connection_magnitude(hparams["hc_magnitude"])
+
+        # is_full is written explicitly; the graph must not infer it from tensor presence
+        is_full = self.indexer_is_full()
+        if is_full is not None:
+            self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
+            self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
+            self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
+            self.gguf_writer.add_indexer_types(is_full)
+            logger.info(
+                "HY_V4 DSA: %d/%d layers own an indexer (top_k=%d, n_heads=%d, head_dim=%d)",
+                sum(is_full), len(is_full), hparams["index_topk"],
+                hparams["index_n_heads"], hparams["index_head_dim"],
+            )
+
+        if hparams.get("num_nextn_predict_layers", 0):
+            logger.warning(
+                "HY_V4: dropping %d MTP (nextn) layer(s) - the reference runs them only under "
+                "training / speculative decoding. This GGUF cannot be used for speculative decoding.",
+                hparams["num_nextn_predict_layers"],
+            )
+
+    def prepare_tensors(self):
+        # validate before the base materializes tensors, so a mismatch fails early
+        is_full = self.indexer_is_full()
+        if is_full is not None:
+            present: dict[int, set[str]] = {}
+            for name in self.model_tensors:
+                m = re.match(r"model\.layers\.(\d+)\.(self_attn\.indexer\..+)$", name)
+                if m:
+                    present.setdefault(int(m.group(1)), set()).add(m.group(2))
+            for il, expect_full in enumerate(is_full):
+                seen = present.get(il, set())
+                if expect_full and seen != self.INDEXER_SUFFIXES:
+                    raise ValueError(
+                        f"HY_V4 layer {il} is indexer_types 'full' but is missing indexer tensors: "
+                        f"{sorted(self.INDEXER_SUFFIXES - seen)}"
+                    )
+                if not expect_full and seen:
+                    raise ValueError(
+                        f"HY_V4 layer {il} is indexer_types 'shared' but carries indexer tensors: "
+                        f"{sorted(seen)}"
+                    )
+
+        super().prepare_tensors()
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # iHC mixing matrices are 2D .weight tensors that the reference keeps in fp32
+        # (_keep_in_fp32_modules_strict). 1D tensors (hc_base/scale, attn_sinks,
+        # e_score_correction_bias) and the router (FFN_GATE_INP) are already forced F32 by the
+        # base rules. Force the HC *_fn matrices here.
+        if new_name.endswith(("hc_attn_fn.weight", "hc_ffn_fn.weight", "output_hc_fn.weight")):
+            return gguf.GGMLQuantizationType.F32
+        # indexer k_norm is fp32 in the reference; the base rules already cover
+        # *_norm.weight and INDEXER_PROJ, but not this bias
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.INDEXER_K_NORM, bid, suffix=".bias"):
+            return gguf.GGMLQuantizationType.F32
+        # enable_lm_head_fp32: mirror the reference fp32 LM-head matmul by keeping output F32.
+        if new_name == "output.weight" and self.hparams.get("enable_lm_head_fp32", False):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
+        hparams = self.hparams
+        n_head = hparams["num_attention_heads"]
+        qk_nope = hparams["qk_nope_head_dim"]
+        v_head_dim = hparams["v_head_dim"]
+        moe_inter = hparams["moe_intermediate_size"]
+
+        tn = self.format_tensor_name
+
+        # ---- global (non per-layer) ----
+        if name == "model.embed_tokens.weight":
+            return [(tn(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch)]
+        if name == "model.norm.weight":
+            return [(tn(gguf.MODEL_TENSOR.OUTPUT_NORM), data_torch)]
+        if name == "lm_head.weight":
+            return [(tn(gguf.MODEL_TENSOR.OUTPUT), data_torch)]
+        if name == "model.hc_head.hc_head_fn":
+            return [(tn(gguf.MODEL_TENSOR.HC_HEAD_FN), data_torch)]
+        if name == "model.hc_head.hc_head_base":
+            return [(tn(gguf.MODEL_TENSOR.HC_HEAD_BASE), data_torch)]
+        if name == "model.hc_head.hc_head_scale":
+            return [(tn(gguf.MODEL_TENSOR.HC_HEAD_SCALE), data_torch)]
+
+        assert bid is not None, f"expected a per-layer tensor, got {name!r}"
+
+        # ---- per-layer, keyed by suffix after 'model.layers.{bid}.' ----
+        suffix = name.split(f"model.layers.{bid}.", 1)[-1]
+
+        # note: q_b_proj and kv_a_proj_with_mqa are mapped straight through (no RoPE permute),
+        # the graph rotates consecutive pairs so the rows need no reordering
+        simple = {
+            "input_layernorm.weight":          (gguf.MODEL_TENSOR.ATTN_NORM, ".weight"),
+            "post_attention_layernorm.weight": (gguf.MODEL_TENSOR.FFN_NORM,  ".weight"),
+            "self_attn.q_a_proj.weight":       (gguf.MODEL_TENSOR.ATTN_Q_A, ".weight"),
+            "self_attn.q_a_layernorm.weight":  (gguf.MODEL_TENSOR.ATTN_Q_A_NORM, ".weight"),
+            "self_attn.q_b_proj.weight":       (gguf.MODEL_TENSOR.ATTN_Q_B, ".weight"),
+            "self_attn.kv_a_proj_with_mqa.weight": (gguf.MODEL_TENSOR.ATTN_KV_A_MQA, ".weight"),
+            "self_attn.kv_a_layernorm.weight": (gguf.MODEL_TENSOR.ATTN_KV_A_NORM, ".weight"),
+            "self_attn.o_proj.weight":         (gguf.MODEL_TENSOR.ATTN_OUT, ".weight"),
+            "self_attn.linear_gate.weight":    (gguf.MODEL_TENSOR.ATTN_GATE, ".weight"),
+            "self_attn.learnable_sink_param":  (gguf.MODEL_TENSOR.ATTN_SINKS, ".weight"),
+            "self_attn.indexer.wq_b.weight":   (gguf.MODEL_TENSOR.INDEXER_ATTN_Q_B, ".weight"),
+            "self_attn.indexer.wk.weight":     (gguf.MODEL_TENSOR.INDEXER_ATTN_K, ".weight"),
+            "self_attn.indexer.k_norm.weight": (gguf.MODEL_TENSOR.INDEXER_K_NORM, ".weight"),
+            "self_attn.indexer.k_norm.bias":   (gguf.MODEL_TENSOR.INDEXER_K_NORM, ".bias"),
+            "self_attn.indexer.weights_proj.weight": (gguf.MODEL_TENSOR.INDEXER_PROJ, ".weight"),
+            "hc_attn_layer.hc_pre.hc_fn":      (gguf.MODEL_TENSOR.HC_ATTN_FN, ".weight"),
+            "hc_attn_layer.hc_pre.hc_base":    (gguf.MODEL_TENSOR.HC_ATTN_BASE, ".weight"),
+            "hc_attn_layer.hc_pre.hc_scale":   (gguf.MODEL_TENSOR.HC_ATTN_SCALE, ".weight"),
+            "hc_mlp_layer.hc_pre.hc_fn":       (gguf.MODEL_TENSOR.HC_FFN_FN, ".weight"),
+            "hc_mlp_layer.hc_pre.hc_base":     (gguf.MODEL_TENSOR.HC_FFN_BASE, ".weight"),
+            "hc_mlp_layer.hc_pre.hc_scale":    (gguf.MODEL_TENSOR.HC_FFN_SCALE, ".weight"),
+            "mlp.gate.weight":                 (gguf.MODEL_TENSOR.FFN_GATE_INP, ".weight"),
+            "mlp.gate.e_score_correction.bias":(gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
+            "mlp.gate_proj.weight":            (gguf.MODEL_TENSOR.FFN_GATE, ".weight"),
+            "mlp.up_proj.weight":              (gguf.MODEL_TENSOR.FFN_UP, ".weight"),
+            "mlp.down_proj.weight":            (gguf.MODEL_TENSOR.FFN_DOWN, ".weight"),
+            "mlp.shared_experts.gate_proj.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
+            "mlp.shared_experts.up_proj.weight":   (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"),
+            "mlp.shared_experts.down_proj.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
+        }
+        if suffix in simple:
+            key, sfx = simple[suffix]
+            return [(tn(key, bid, sfx), data_torch)]
+
+        # kv_b_proj: split into k_b (transposed) and v_b
+        if suffix == "self_attn.kv_b_proj.weight":
+            k_b, v_b = split_kv_b_proj(data_torch, n_head, qk_nope, v_head_dim)
+            return [
+                (tn(gguf.MODEL_TENSOR.ATTN_K_B, bid), k_b),
+                (tn(gguf.MODEL_TENSOR.ATTN_V_B, bid), v_b),
+            ]
+
+        # fused stacked experts: split gate_up into gate/up
+        if suffix == "mlp.experts.gate_up_proj":
+            gate, up = split_gate_up(data_torch, moe_inter)
+            return [
+                (tn(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
+                (tn(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
+            ]
+        if suffix == "mlp.experts.down_proj":
+            return [(tn(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid), data_torch)]
+
+        raise ValueError(f"Unsupported HY_V4 tensor {name!r} (suffix {suffix!r})")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index e5d3196efe4..c4141afa614 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -176,6 +176,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
     {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
     {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
+    {"name": "hy_v4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hy4-preview", "chkhsh": "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c"},
     # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index b85f62a3114..399d31f1d55 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -230,6 +230,8 @@ class HyperConnection:
         COUNT                = "{arch}.hyper_connection.count"
         SINKHORN_ITERATIONS  = "{arch}.hyper_connection.sinkhorn_iterations"
         EPSILON              = "{arch}.hyper_connection.epsilon"
+        # scale of the post gate (DeepSeek-V4 hardcodes 2.0)
+        MAGNITUDE            = "{arch}.hyper_connection.magnitude"
         # absent means the mix projection is full rank (DeepSeek-V4 behaviour)
         LOW_RANK             = "{arch}.hyper_connection.low_rank"
 
@@ -592,6 +594,7 @@ class MODEL_ARCH(IntEnum):
     HUNYUAN_DENSE    = auto()
     HUNYUAN_VL       = auto()
     HY_V3            = auto()
+    HY_V4            = auto()
     SMOLLM3          = auto()
     GPT_OSS          = auto()
     LFM2             = auto()
@@ -1345,6 +1348,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
     MODEL_ARCH.HUNYUAN_VL:       "hunyuan_vl",
     MODEL_ARCH.HY_V3:            "hy_v3",
+    MODEL_ARCH.HY_V4:            "hy_v4",
     MODEL_ARCH.SMOLLM3:          "smollm3",
     MODEL_ARCH.GPT_OSS:          "gpt-oss",
     MODEL_ARCH.LFM2:             "lfm2",
@@ -4739,6 +4743,48 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
         MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
+    MODEL_ARCH.HY_V4: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.HC_HEAD_FN,
+        MODEL_TENSOR.HC_HEAD_BASE,
+        MODEL_TENSOR.HC_HEAD_SCALE,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_SINKS,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.INDEXER_K_NORM,
+        MODEL_TENSOR.INDEXER_PROJ,
+        MODEL_TENSOR.INDEXER_ATTN_K,
+        MODEL_TENSOR.INDEXER_ATTN_Q_B,
+        MODEL_TENSOR.HC_ATTN_FN,
+        MODEL_TENSOR.HC_ATTN_BASE,
+        MODEL_TENSOR.HC_ATTN_SCALE,
+        MODEL_TENSOR.HC_FFN_FN,
+        MODEL_TENSOR.HC_FFN_BASE,
+        MODEL_TENSOR.HC_FFN_SCALE,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     MODEL_ARCH.SMOLLM3: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -5438,6 +5484,10 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ROPE_FREQS,
         MODEL_TENSOR.ATTN_ROT_EMBD,
     ],
+    MODEL_ARCH.HY_V4: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
     MODEL_ARCH.CHATGLM: [
         MODEL_TENSOR.ROPE_FREQS,
     ],
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 689c2fca111..50e4d7c534a 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -1055,6 +1055,9 @@ def add_hyper_connection_sinkhorn_iterations(self, count: int) -> None:
     def add_hyper_connection_epsilon(self, value: float) -> None:
         self.add_float32(Keys.HyperConnection.EPSILON.format(arch=self.arch), value)
 
+    def add_hyper_connection_magnitude(self, value: float) -> None:
+        self.add_float32(Keys.HyperConnection.MAGNITUDE.format(arch=self.arch), value)
+
     def add_hyper_connection_low_rank(self, value: int) -> None:
         self.add_uint32(Keys.HyperConnection.LOW_RANK.format(arch=self.arch), value)
 
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 446de4ae25b..d06be641a2b 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -121,6 +121,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_HUNYUAN_DENSE,    "hunyuan-dense"    },
     { LLM_ARCH_HUNYUAN_VL,       "hunyuan_vl"       },
     { LLM_ARCH_HY_V3,            "hy_v3"            },
+    { LLM_ARCH_HY_V4,            "hy_v4"            },
     { LLM_ARCH_SMOLLM3,          "smollm3"          },
     { LLM_ARCH_OPENAI_MOE,       "gpt-oss"          },
     { LLM_ARCH_LFM2,             "lfm2"             },
@@ -294,6 +295,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_HYPER_CONNECTION_COUNT,                 "%s.hyper_connection.count"                 },
     { LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS,   "%s.hyper_connection.sinkhorn_iterations"   },
     { LLM_KV_HYPER_CONNECTION_EPSILON,               "%s.hyper_connection.epsilon"               },
+    { LLM_KV_HYPER_CONNECTION_MAGNITUDE,             "%s.hyper_connection.magnitude"             },
     { LLM_KV_HYPER_CONNECTION_LOW_RANK,              "%s.hyper_connection.low_rank"              },
 
     { LLM_KV_PLE_LAYERS,                             "%s.ple.layers"                             },
@@ -1130,6 +1132,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_OLMOE:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
+        case LLM_ARCH_HY_V4:
         case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_BITNET:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 0c0b994836f..62dfa5d817d 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -126,6 +126,7 @@ enum llm_arch {
     LLM_ARCH_HUNYUAN_DENSE,
     LLM_ARCH_HUNYUAN_VL,
     LLM_ARCH_HY_V3,
+    LLM_ARCH_HY_V4,
     LLM_ARCH_SMOLLM3,
     LLM_ARCH_OPENAI_MOE,
     LLM_ARCH_LFM2,
@@ -299,6 +300,7 @@ enum llm_kv {
     LLM_KV_HYPER_CONNECTION_COUNT,
     LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS,
     LLM_KV_HYPER_CONNECTION_EPSILON,
+    LLM_KV_HYPER_CONNECTION_MAGNITUDE,
     LLM_KV_HYPER_CONNECTION_LOW_RANK,
 
     LLM_KV_PLE_LAYERS,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 3cc27717ece..c1ef12f56ba 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2317,7 +2317,8 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
         (model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
         model.arch == LLM_ARCH_NANBEIGE ||
         model.arch == LLM_ARCH_MINIMAX_01 ||
-        model.arch == LLM_ARCH_MINIMAX_M3) {
+        model.arch == LLM_ARCH_MINIMAX_M3 ||
+        model.arch == LLM_ARCH_HY_V4) {
         res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     } else if (model.arch == LLM_ARCH_DFLASH && model.hparams.dflash_selector_rank > 0) {
         // DFlash2's convolutions and selector are shape work rather than matmuls,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 274a6264336..8ea441f441d 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -566,7 +566,10 @@ void llm_graph_input_attn_k_dsa::set_input(const llama_ubatch * ubatch) {
 
     mctx->get_lid()->set_input_kq_mask(self_kq_mask_lid, ubatch, cparams.causal_attn);
 
-    mctx->get_lid()->set_input_k_rot(self_k_rot_lid);
+    // left unallocated when the indexer does not use the rotation
+    if (self_k_rot_lid && self_k_rot_lid->buffer) {
+        mctx->get_lid()->set_input_k_rot(self_k_rot_lid);
+    }
 }
 
 bool llm_graph_input_attn_k_dsa::can_reuse(const llm_graph_params & params) {
@@ -2170,7 +2173,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                     const float limit = hparams.swiglu_clamp_exp[il];
                     constexpr float eps = 1e-6f;
                     if (limit > eps) {
-                        if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0)) {
+                        if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) {
                             cur = ggml_swiglu_clamp(ctx0, cur, up, limit);
                         } else {
                             up = ggml_clamp(ctx0, up, -limit, limit);
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 2f238a1744c..d982a031037 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -289,6 +289,9 @@ struct llama_hparams {
     // 0 = full rank (DeepSeek-V4)
     uint32_t hc_low_rank = 0;
 
+    // scale of the hyper-connection post gate (DeepSeek-V4 hardcodes 2.0)
+    float    hc_magnitude = 0.0f;
+
     uint32_t ple_ngram_size      = 0;
     uint32_t ple_heads_per_ngram = 0;
     uint32_t ple_conv_kernel     = 0;
diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp
index 919e90ecccd..df2a46d932b 100644
--- a/src/llama-model-saver.cpp
+++ b/src/llama-model-saver.cpp
@@ -314,6 +314,7 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_HYPER_CONNECTION_COUNT,               hparams.dsv4_hc_mult);
     add_kv(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
     add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,             hparams.dsv4_hc_eps);
+    add_kv(LLM_KV_HYPER_CONNECTION_MAGNITUDE,           hparams.hc_magnitude);
     add_kv(LLM_KV_HASH_LAYER_COUNT,                     hparams.dsv4_hash_layer_count);
     add_kv(LLM_KV_HYPER_CONNECTION_LOW_RANK,             hparams.hc_low_rank);
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 6344f2d8aee..f8f18c83dec 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -288,6 +288,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_hunyuan_dense(params);
         case LLM_ARCH_HY_V3:
             return new llama_model_hy_v3(params);
+        case LLM_ARCH_HY_V4:
+            return new llama_model_hy_v4(params);
         case LLM_ARCH_SMOLLM3:
             return new llama_model_smollm3(params);
         case LLM_ARCH_OPENAI_MOE:
@@ -2056,7 +2058,8 @@ void llama_model::print_info() const {
 
         if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR ||
                 arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA ||
-                arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4) {
+                arch == LLM_ARCH_DOTS3NOTE || arch == LLM_ARCH_MISTRAL4 ||
+                arch == LLM_ARCH_HY_V4) {
             LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
             LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
             LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
@@ -2325,6 +2328,48 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             nullptr);
                 }
             } break;
+        case LLM_ARCH_HY_V4:
+            {
+                if (hparams.indexer_top_k == 0) {
+                    // full-attention checkpoint: no indexer, so no indexer key cache
+                    res = new llama_kv_cache(
+                            *this,
+                            hparams,
+                            params.type_k,
+                            params.type_v,
+                            !cparams.flash_attn,
+                            cparams.offload_kqv,
+                            cparams.kv_unified,
+                            cparams.n_ctx_seq,
+                            cparams.n_seq_max,
+                            1,
+                            hparams.n_swa,
+                            hparams.swa_type,
+                            nullptr,
+                            nullptr,
+                            nullptr,
+                            nullptr);
+                } else {
+                    // only "full" layers own an indexer, so the shared layers need no indexer cache
+                    llama_kv_cache::layer_filter_cb filter_lid = [&](uint32_t il) { return hparams.is_indexer_full(il); };
+
+                    res = new llama_kv_cache_dsa(
+                            *this,
+                            params.type_k,
+                            params.type_v,
+                            !cparams.flash_attn,
+                            cparams.offload_kqv,
+                            cparams.kv_unified,
+                            cparams.n_ctx_seq,
+                            cparams.n_seq_max,
+                            1,
+                            hparams.n_swa,
+                            hparams.swa_type,
+                            nullptr,
+                            filter_lid,
+                            nullptr);
+                }
+            } break;
         case LLM_ARCH_DOTS3NOTE:
             {
                 GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
@@ -2884,6 +2929,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_DOTS3NOTE:
         case LLM_ARCH_NANBEIGE:
         case LLM_ARCH_POCKETTTS:
+        // HY_V4 rotates consecutive pairs, matching the reference implementation
+        case LLM_ARCH_HY_V4:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index ff926ceecd1..c0c34cdd8cd 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -318,6 +318,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
             case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
             case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
             case LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM:
+            case LLAMA_VOCAB_PRE_TYPE_HY_V4:
                 regex_exprs = {
                     "\\p{N}{1,3}",
                     "[一-龥぀-ゟ゠-ヿ]+",
@@ -2350,6 +2351,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "hunyuan-dense") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "hy_v4") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_HY_V4;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "joyai-llm") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_JOYAI_LLM;
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index b7c28926338..e02ea78ffae 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -65,6 +65,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54,
     LLAMA_VOCAB_PRE_TYPE_MELLUM2           = 55,
     LLAMA_VOCAB_PRE_TYPE_LAGUNA            = 56,
+    LLAMA_VOCAB_PRE_TYPE_HY_V4             = 57,
 };
 
 struct LLM_KV;
diff --git a/src/models/hy-v4.cpp b/src/models/hy-v4.cpp
new file mode 100644
index 00000000000..ee41787ba3e
--- /dev/null
+++ b/src/models/hy-v4.cpp
@@ -0,0 +1,601 @@
+#include "models.h"
+
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-dsa.h"
+
+#include <cmath>
+
+// iHC (independent Hyper-Connections) helpers. Same layout as the DeepSeek-V4 HC, but without
+// the comb/sinkhorn term: hc_fn makes only 2*hc coefficients (pre + post). The streams mix
+// through the pre-reduce / post-distribute round trip instead.
+
+static size_t hy_v4_elem_offset(const ggml_tensor * t, int64_t i) {
+    return ggml_row_size(t->type, i);
+}
+
+static ggml_tensor * hy_v4_view_1d(ggml_context * ctx, ggml_tensor * t, int64_t ne0, int64_t i0) {
+    return ggml_view_1d(ctx, t, ne0, hy_v4_elem_offset(t, i0));
+}
+
+static ggml_tensor * hy_v4_view_2d(ggml_context * ctx, ggml_tensor * t, int64_t ne0, int64_t ne1, int64_t i0) {
+    return ggml_view_2d(ctx, t, ne0, ne1, t->nb[1], hy_v4_elem_offset(t, i0));
+}
+
+void llama_model_hy_v4::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
+    ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK,      hparams.n_lora_kv);
+    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA,    hparams.n_embd_head_k_mla_impl);
+    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,  hparams.n_embd_head_v_mla_impl);
+    ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm,  false);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func,   false);
+
+    // routed-expert SwiGLU logits clamp (shared/dense experts are NOT clamped, so
+    // swiglu_clamp_shexp is intentionally left at its 0 default)
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all, false);
+
+    ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT,     hparams.dsv4_hc_mult);
+    ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON,   hparams.dsv4_hc_eps);
+    ml.get_key(LLM_KV_HYPER_CONNECTION_MAGNITUDE, hparams.hc_magnitude);
+
+    // DSA is absent on the all-full_attention checkpoints, so indexer_top_k stays 0 there
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head,    false);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size, false);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k,     false);
+
+    if (hparams.indexer_top_k > 0) {
+        // the reference plumbs rms_norm_eps into the indexer k_norm LayerNorm, and build_norm
+        // reads f_norm_eps for LLM_NORM
+        hparams.f_norm_eps = hparams.f_norm_rms_eps;
+
+        if (hparams.indexer_n_head == 0 || hparams.indexer_head_size <= hparams.n_rot()) {
+            throw std::runtime_error("hy_v4: bad indexer head count / key length");
+        }
+
+        ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
+        if (!hparams.is_indexer_full(0)) {
+            throw std::runtime_error("hy_v4: layer 0 must own an indexer, nothing precedes it to share");
+        }
+    }
+
+    GGML_ASSERT(hparams.is_mla());
+
+    type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_hy_v4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_head_k_mla   = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v_mla   = hparams.n_embd_head_v_mla();
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
+    GGML_ASSERT(n_embd_head_qk_nope >= 1);
+
+    const int64_t q_lora_rank     = hparams.n_lora_q;
+    const int64_t kv_lora_rank    = hparams.n_lora_kv;
+    const int64_t n_ff_exp        = hparams.n_ff_exp();
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+    const int64_t hc              = hparams.dsv4_hc_mult;
+
+    tok_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    // global iHC head (collapses hc streams before the final norm)
+    hc_head_fn    = create_tensor(tn(LLM_TENSOR_HC_HEAD_FN,    "weight"), {hc * n_embd, hc}, 0);
+    hc_head_base  = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE,  "weight"), {hc}, 0);
+    hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
+        layer.attn_sinks    = create_tensor(tn(LLM_TENSOR_ATTN_SINKS,    "weight", i), {n_head}, 0);
+
+        layer.wq_a          = create_tensor(tn(LLM_TENSOR_ATTN_Q_A,      "weight", i), {n_embd, q_lora_rank}, 0);
+        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+        layer.wq_b          = create_tensor(tn(LLM_TENSOR_ATTN_Q_B,      "weight", i), {q_lora_rank, n_head * n_embd_head_k_mla}, 0);
+        layer.wkv_a_mqa     = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, 0);
+        layer.attn_kv_a_norm= create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM,"weight", i), {kv_lora_rank}, 0);
+        layer.wk_b          = create_tensor(tn(LLM_TENSOR_ATTN_K_B,      "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
+        layer.wv_b          = create_tensor(tn(LLM_TENSOR_ATTN_V_B,      "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head}, 0);
+        layer.wo            = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_head * n_embd_head_v_mla, n_embd}, 0);
+        layer.wqkv_gate     = create_tensor(tn(LLM_TENSOR_ATTN_GATE,     "weight", i), {n_embd, n_head * n_embd_head_v_mla}, 0);
+
+        // only "full" indexer layers ship weights; "shared" layers reuse their top-k
+        if (hparams.indexer_top_k > 0 && hparams.is_indexer_full(i)) {
+            const int64_t n_indexer_head = hparams.indexer_n_head;
+            const int64_t n_embd_indexer = hparams.indexer_head_size;
+
+            layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, n_indexer_head * n_embd_indexer}, 0);
+            layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, n_embd_indexer}, 0);
+            layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {n_embd_indexer}, 0);
+            layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {n_embd_indexer}, 0);
+            layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, n_indexer_head}, 0);
+        }
+
+        layer.hc_attn_fn    = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN,    "weight", i), {hc * n_embd, 2 * hc}, 0);
+        layer.hc_attn_base  = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE,  "weight", i), {2 * hc}, 0);
+        layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {2}, 0);
+        layer.hc_ffn_fn     = create_tensor(tn(LLM_TENSOR_HC_FFN_FN,     "weight", i), {hc * n_embd, 2 * hc}, 0);
+        layer.hc_ffn_base   = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE,   "weight", i), {2 * hc}, 0);
+        layer.hc_ffn_scale  = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE,  "weight", i), {2}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if (i < (int) hparams.n_layer_dense_lead) {
+            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);
+        } else {
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+            if (n_expert == 0) {
+                throw std::runtime_error("n_expert must be > 0");
+            }
+            if (n_expert_used == 0) {
+                throw std::runtime_error("n_expert_used must be > 0");
+            }
+
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
+
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_hy_v4::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+// reduce hc streams x[:,i,:] weighted by w[i,:] -> [n_embd, n_tokens]
+// reference runs this in fp32 (inside the float() / autocast(fp32) context)
+static ggml_tensor * hy_v4_hc_reduce(ggml_context * ctx0, ggml_tensor * x, ggml_tensor * w, int64_t hc, int64_t n_embd, int64_t nt, ggml_type out_type) {
+    ggml_tensor * x_f32 = ggml_cast(ctx0, x, GGML_TYPE_F32);
+    ggml_tensor * result = nullptr;
+    for (int64_t ih = 0; ih < hc; ++ih) {
+        ggml_tensor * xh = ggml_view_2d(ctx0, x_f32, n_embd, nt, x_f32->nb[2], ih * x_f32->nb[1]);
+        ggml_tensor * wh = ggml_view_2d(ctx0, w, 1, nt, w->nb[1], ih * w->nb[0]);
+        ggml_tensor * cur = ggml_mul(ctx0, xh, wh);
+        result = result ? ggml_add(ctx0, result, cur) : cur;
+    }
+    return ggml_cast(ctx0, result, out_type);
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_hc_pre(
+        ggml_tensor * x,
+        ggml_tensor * hc_fn,
+        ggml_tensor * hc_scale,
+        ggml_tensor * hc_base,
+        ggml_tensor ** post,
+        int il) const {
+    const int64_t hc  = hparams.dsv4_hc_mult;
+    const int64_t nt  = x->ne[2];
+    GGML_ASSERT(x->ne[0] == n_embd && x->ne[1] == hc);
+
+    ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc * n_embd, nt);
+    ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, hparams.f_norm_rms_eps);
+    ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm); // [2*hc, nt]
+    cb(mixes, "hc_mixes", il);
+
+    ggml_tensor * scale_pre  = hy_v4_view_1d(ctx0, hc_scale, 1, 0);
+    ggml_tensor * scale_post = hy_v4_view_1d(ctx0, hc_scale, 1, 1);
+    ggml_tensor * base_pre   = hy_v4_view_1d(ctx0, hc_base, hc, 0);
+    ggml_tensor * base_post  = hy_v4_view_1d(ctx0, hc_base, hc, hc);
+
+    // pre = sigmoid(mixes[:hc]*scale_pre + base_pre) + eps
+    ggml_tensor * pre = hy_v4_view_2d(ctx0, mixes, hc, nt, 0);
+    pre = ggml_mul(ctx0, pre, scale_pre);
+    pre = ggml_add(ctx0, pre, base_pre);
+    pre = ggml_sigmoid(ctx0, pre);
+    pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
+    cb(pre, "hc_pre", il);
+
+    // post = magnitude*sigmoid(mixes[hc:2hc]*scale_post + base_post) + eps
+    ggml_tensor * po = hy_v4_view_2d(ctx0, mixes, hc, nt, hc);
+    po = ggml_mul(ctx0, po, scale_post);
+    po = ggml_add(ctx0, po, base_post);
+    po = ggml_sigmoid(ctx0, po);
+    po = ggml_scale(ctx0, po, hparams.hc_magnitude);
+    po = ggml_scale_bias(ctx0, po, 1.0f, hparams.dsv4_hc_eps);
+    *post = po;
+    cb(po, "hc_post_gate", il);
+
+    return hy_v4_hc_reduce(ctx0, x, pre, hc, n_embd, nt, x->type);
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_hc_post(
+        ggml_tensor * x,
+        ggml_tensor * residual,
+        ggml_tensor * post,
+        int il) const {
+    GGML_UNUSED(il);
+    const int64_t hc = hparams.dsv4_hc_mult;
+    const int64_t nt = x->ne[1];
+    GGML_ASSERT(x->ne[0] == n_embd);
+    GGML_ASSERT(residual->ne[1] == hc);
+
+    // reference HC post runs entirely in fp32 to avoid bf16 rounding accumulation
+    // across 78 layers: post.float() * x.float() + residual.float() -> .to(dtype)
+    ggml_tensor * x_f32    = ggml_cast(ctx0, x, GGML_TYPE_F32);
+    ggml_tensor * post_f32 = ggml_cast(ctx0, post, GGML_TYPE_F32);
+    ggml_tensor * res_f32  = ggml_cast(ctx0, residual, GGML_TYPE_F32);
+
+    ggml_tensor * out = nullptr;
+    for (int64_t i = 0; i < hc; ++i) {
+        ggml_tensor * res_i  = ggml_view_2d(ctx0, res_f32, n_embd, nt, res_f32->nb[2], i * res_f32->nb[1]);
+        ggml_tensor * post_i = ggml_view_2d(ctx0, post_f32, 1, nt, post_f32->nb[1], i * post_f32->nb[0]);
+        ggml_tensor * cur = ggml_add(ctx0, res_i, ggml_mul(ctx0, x_f32, post_i));
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, nt);
+        out = out ? ggml_concat(ctx0, out, cur, 1) : cur;
+    }
+
+    // cast back to the original type (bf16)
+    out = ggml_cast(ctx0, out, residual->type);
+    return out; // [n_embd, hc, nt]
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_hc_head(
+        ggml_tensor * x,
+        ggml_tensor * hc_fn,
+        ggml_tensor * hc_scale,
+        ggml_tensor * hc_base) const {
+    const int64_t hc = hparams.dsv4_hc_mult;
+    const int64_t nt = x->ne[2];
+
+    ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc * n_embd, nt);
+    ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, hparams.f_norm_rms_eps);
+    ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm); // [hc, nt]
+    cb(mixes, "hc_head_mixes", -1);
+
+    ggml_tensor * pre = ggml_mul(ctx0, mixes, hc_scale);
+    pre = ggml_add(ctx0, pre, hc_base);
+    pre = ggml_sigmoid(ctx0, pre);
+    pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
+    cb(pre, "hc_head_pre", -1);
+
+    return hy_v4_hc_reduce(ctx0, x, pre, hc, n_embd, nt, x->type);
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_attention(
+        const llama_model & model,
+        llm_graph_input_attn_k * inp_attn,
+        ggml_tensor * cur,
+        ggml_tensor * inp_pos,
+        float kq_scale,
+        int il) const {
+    const auto & layer = model.layers[il];
+
+    const int64_t n_embd_head_k       = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
+    const uint32_t kv_lora_rank       = hparams.n_lora_kv;
+
+    ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
+    q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+    q = ggml_mul_mat(ctx0, layer.wq_b, q);
+
+    ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+            ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
+    ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+            ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head,
+            ggml_row_size(q->type, n_embd_head_qk_nope));
+
+    ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
+    ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+    ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+
+    q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    cb(q_pe, "q_pe", il);
+    k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    cb(k_pe, "k_pe", il);
+
+    kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+    cb(kv_cmpr, "kv_cmpr", il);
+
+    // MLA absorption: q_nope @ wk_b -> compressed space
+    q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+    ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
+    q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+
+    // note: rope must go first for in-place context shifting in build_rope_shift()
+    ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+
+    kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+    ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
+    ggml_tensor * Vcur = kv_cmpr;
+
+    // MLA-as-MQA; wo applied manually below so the gated-MLA gate can sit before o_proj
+    ggml_tensor * attn = build_attn(inp_attn,
+            nullptr, nullptr, nullptr,
+            Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, layer.wv_b, kq_scale, il);
+    cb(attn, "attn_kqv", il); // [n_head * n_embd_head_v, n_tokens]
+
+    // gated MLA: elementwise sigmoid gate on the decompressed attention output
+    ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur);
+    gate = ggml_sigmoid(ctx0, gate);
+    attn = ggml_mul(ctx0, attn, gate);
+    cb(attn, "attn_gated", il);
+
+    ggml_tensor * out = build_lora_mm(layer.wo, attn);
+    cb(out, "attn_out", il);
+
+    return out;
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_indexer_top_k(
+        const llama_model & model,
+        llm_graph_input_attn_k_dsa * inp_attn_dsa,
+        ggml_tensor * cur,
+        ggml_tensor * qr,
+        ggml_tensor * inp_pos,
+        int il) const {
+    const auto & layer = model.layers[il];
+
+    const int64_t n_indexer_head       = hparams.indexer_n_head;
+    const int64_t n_embd_indexer       = hparams.indexer_head_size;
+    const int64_t n_embd_indexer_rope  = hparams.n_rot();
+    const int64_t n_embd_indexer_nope  = n_embd_indexer - n_embd_indexer_rope;
+
+    // nope rows come first, so rope only the last n_embd_indexer_rope rows, same as the MLA path
+    ggml_tensor * iq = ggml_mul_mat(ctx0, layer.indexer_attn_q_b, qr);
+
+    iq = ggml_reshape_3d(ctx0, iq, n_embd_indexer, n_indexer_head, n_tokens);
+
+    iq = ggml_rope_ext(ctx0, iq, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base,
+         freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    iq = ggml_rope_set_offset(iq, n_embd_indexer_nope);
+    cb(iq, "indexer_q", il);
+
+    ggml_tensor * ik = ggml_mul_mat(ctx0, layer.indexer_attn_k, cur);
+
+    ik = build_norm(ik, layer.indexer_k_norm, layer.indexer_k_norm_b, LLM_NORM, il);
+
+    ik = ggml_reshape_3d(ctx0, ik, n_embd_indexer, 1, n_tokens);
+
+    ik = ggml_rope_ext(ctx0, ik, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base,
+         freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+    ik = ggml_rope_set_offset(ik, n_embd_indexer_nope);
+    cb(ik, "indexer_k", il);
+
+    // the reference applies a Hadamard rotation here, but it only helps its FP8 kernels.
+    // it is orthogonal, so it does not change q.k and we can skip it.
+
+    const auto * mctx_lid   = inp_attn_dsa->mctx->get_lid();
+    const auto & k_idxs_lid = inp_attn_dsa->get_k_idxs_lid();
+    ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, ik, k_idxs_lid, il));
+
+    ggml_tensor * iw = ggml_mul_mat(ctx0, layer.indexer_proj, cur);
+
+    ik = mctx_lid->get_k(ctx0, il);
+
+    const auto n_stream = ik->ne[3];
+    iq = ggml_view_4d(ctx0, iq, iq->ne[0], iq->ne[1], iq->ne[2]/n_stream, n_stream,
+            iq->nb[1], iq->nb[2], iq->nb[3]/n_stream, 0);
+    iw = ggml_view_4d(ctx0, iw, iw->ne[0], iw->ne[1]/n_stream, iw->ne[2], n_stream,
+            iw->nb[1], iw->nb[2]/n_stream, iw->nb[3]/n_stream, 0);
+
+    // fold both reference scale factors into the weights before the big score tensor
+    iw = ggml_scale(ctx0, iw, 1.0f / sqrtf(float(n_embd_indexer * n_indexer_head)));
+
+    ggml_tensor * score = nullptr;
+    if (cparams.fused_lid) {
+        score = ggml_lightning_indexer(ctx0, iq, ik, iw, inp_attn_dsa->get_kq_mask_lid());
+        cb(score, "indexer_score", il);
+        res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, score, il});
+    } else {
+        iq = ggml_permute(ctx0, iq, 0, 2, 1, 3);
+        ik = ggml_permute(ctx0, ik, 0, 2, 1, 3);
+
+        score = ggml_mul_mat(ctx0, ik, iq);
+        score = ggml_cont(ctx0, ggml_permute(ctx0, score, 2, 1, 0, 3));
+        score = ggml_relu(ctx0, score);
+        score = ggml_mul(ctx0, score, iw);
+        score = ggml_sum_rows(ctx0, score);
+        score = ggml_cont(ctx0, ggml_permute(ctx0, score, 2, 1, 0, 3));
+        score = ggml_add(ctx0, score, inp_attn_dsa->get_kq_mask_lid());
+        cb(score, "indexer_score", il);
+    }
+
+    const uint32_t n_top_k = score->ne[0] < (int64_t) hparams.indexer_top_k ? score->ne[0] : hparams.indexer_top_k;
+
+    return ggml_cont(ctx0, ggml_top_k(ctx0, score, n_top_k));
+}
+
+ggml_tensor * llama_model_hy_v4::graph::build_attention_dsa(
+        const llama_model & model,
+        llm_graph_input_attn_k_dsa * inp_attn_dsa,
+        ggml_tensor * cur,
+        ggml_tensor * inp_pos,
+        ggml_tensor ** last_top_k,
+        float kq_scale,
+        int il) const {
+    const auto & layer = model.layers[il];
+
+    const int64_t n_embd_head_k       = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_qk_rope = hparams.n_rot();
+    const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
+    const uint32_t kv_lora_rank       = hparams.n_lora_kv;
+
+    ggml_tensor * qr = ggml_mul_mat(ctx0, layer.wq_a, cur);
+    qr = build_norm(qr, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+
+    if (hparams.is_indexer_full(il)) {
+        *last_top_k = build_indexer_top_k(model, inp_attn_dsa, cur, qr, inp_pos, il);
+        cb(*last_top_k, "top_k", il);
+    }
+    GGML_ASSERT(*last_top_k != nullptr);
+
+    ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_b, qr);
+
+    ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+            ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
+    ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+            ggml_row_size(q->type, n_embd_head_k), ggml_row_size(q->type, n_embd_head_k) * n_head,
+            ggml_row_size(q->type, n_embd_head_qk_nope));
+
+    ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
+    ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+    ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+            ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+
+    q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    cb(q_pe, "q_pe", il);
+    k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+            ext_factor, attn_factor, beta_fast, beta_slow);
+    cb(k_pe, "k_pe", il);
+
+    kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+    cb(kv_cmpr, "kv_cmpr", il);
+
+    q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+    ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
+    q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+
+    ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
+
+    kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+    ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
+    ggml_tensor * Vcur = kv_cmpr;
+
+    ggml_tensor * attn = build_attn(inp_attn_dsa,
+            nullptr, nullptr, nullptr,
+            Qcur, Kcur, Vcur, nullptr, layer.attn_sinks, layer.wv_b, *last_top_k, kq_scale, il);
+    cb(attn, "attn_kqv", il);
+
+    ggml_tensor * gate = ggml_mul_mat(ctx0, layer.wqkv_gate, cur);
+    gate = ggml_sigmoid(ctx0, gate);
+    attn = ggml_mul(ctx0, attn, gate);
+    cb(attn, "attn_gated", il);
+
+    ggml_tensor * out = build_lora_mm(layer.wo, attn);
+    cb(out, "attn_out", il);
+
+    return out;
+}
+
+llama_model_hy_v4::graph::graph(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t hc = hparams.dsv4_hc_mult;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
+    const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
+
+    ggml_tensor * cur;
+
+    const bool is_dsa = hparams.indexer_top_k > 0;
+
+    ggml_tensor * inp = build_inp_embd(model.tok_embd);
+    ggml_tensor * inp_pos = build_inp_pos();
+    llm_graph_input_attn_k     * inp_attn     = is_dsa ? nullptr : build_attn_inp_k();
+    llm_graph_input_attn_k_dsa * inp_attn_dsa = is_dsa ? build_attn_inp_k_dsa() : nullptr;
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    // top-k of the last "full" indexer layer, reused by the following "shared" layers
+    ggml_tensor * last_top_k = nullptr;
+
+    // expand the single embedding into hc parallel residual streams
+    ggml_tensor * inpL = ggml_reshape_3d(ctx0, inp, n_embd, 1, n_tokens);
+    inpL = ggml_repeat_4d(ctx0, inpL, n_embd, hc, n_tokens, 1);
+    cb(inpL, "hc_init", -1);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * residual = inpL;
+        ggml_tensor * post = nullptr;
+
+        cur = build_hc_pre(inpL, model.layers[il].hc_attn_fn, model.layers[il].hc_attn_scale,
+                model.layers[il].hc_attn_base, &post, il);
+        cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        cur = is_dsa
+            ? build_attention_dsa(model, inp_attn_dsa, cur, inp_pos, &last_top_k, kq_scale, il)
+            : build_attention(model, inp_attn, cur, inp_pos, kq_scale, il);
+
+        inpL = build_hc_post(cur, residual, post, il);
+        cb(inpL, "hc_attn_out", il);
+
+        residual = inpL;
+        cur = build_hc_pre(inpL, model.layers[il].hc_ffn_fn, model.layers[il].hc_ffn_scale,
+                model.layers[il].hc_ffn_base, &post, il);
+        cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        const auto & layer = model.layers[il];
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                    layer.ffn_up,   NULL, NULL,
+                    layer.ffn_gate, NULL, NULL,
+                    layer.ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    layer.ffn_gate_inp,
+                    layer.ffn_up_exps,
+                    layer.ffn_gate_exps,
+                    layer.ffn_down_exps,
+                    layer.ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, hparams.expert_weights_norm,
+                    hparams.expert_weights_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il,
+                    nullptr,
+                    nullptr);
+            cb(moe_out, "ffn_moe_out", il);
+
+            ggml_tensor * ffn_shexp = build_ffn(cur,
+                    layer.ffn_up_shexp,   NULL, NULL,
+                    layer.ffn_gate_shexp, NULL, NULL,
+                    layer.ffn_down_shexp, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        }
+
+        inpL = build_hc_post(cur, residual, post, il);
+        cb(inpL, "l_out", il);
+    }
+
+    // prune to the requested output rows once, after all HC streams are done
+    if (inp_out_ids) {
+        ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd * hc, n_tokens);
+        flat = ggml_get_rows(ctx0, flat, inp_out_ids);
+        inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs);
+    }
+
+    cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
+    cb(cur, "hc_head", -1);
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index 9b87a40d5af..93a6b34945d 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -1981,6 +1981,69 @@ struct llama_model_hy_v3 : public llama_model_base {
 };
 
 
+struct llama_model_hy_v4 : public llama_model_base {
+    llama_model_hy_v4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        // iHC (independent Hyper-Connections): pre reduces the hc streams to one and returns the
+        // per-stream post gates, post writes the sublayer output back into the streams, head
+        // collapses the streams before the final norm.
+        ggml_tensor * build_hc_pre(
+                ggml_tensor * x,
+                ggml_tensor * hc_fn,
+                ggml_tensor * hc_scale,
+                ggml_tensor * hc_base,
+                ggml_tensor ** post,
+                int il) const;
+
+        ggml_tensor * build_hc_post(
+                ggml_tensor * x,
+                ggml_tensor * residual,
+                ggml_tensor * post,
+                int il) const;
+
+        ggml_tensor * build_hc_head(
+                ggml_tensor * x,
+                ggml_tensor * hc_fn,
+                ggml_tensor * hc_scale,
+                ggml_tensor * hc_base) const;
+
+        ggml_tensor * build_attention(
+                const llama_model & model,
+                llm_graph_input_attn_k * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                float kq_scale,
+                int il) const;
+
+        // DSA lightning indexer: top-k KV positions for this layer. Only "full" layers compute
+        // it, "shared" layers reuse the last preceding full layer result through last_top_k.
+        ggml_tensor * build_indexer_top_k(
+                const llama_model & model,
+                llm_graph_input_attn_k_dsa * inp_attn_dsa,
+                ggml_tensor * cur,
+                ggml_tensor * qr,
+                ggml_tensor * inp_pos,
+                int il) const;
+
+        ggml_tensor * build_attention_dsa(
+                const llama_model & model,
+                llm_graph_input_attn_k_dsa * inp_attn_dsa,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                ggml_tensor ** last_top_k,
+                float kq_scale,
+                int il) const;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_hunyuan_vl : public llama_model_base {
     llama_model_hunyuan_vl(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index b2ea245ab84..0f3d1c79a7f 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -118,7 +118,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
             || arch == LLM_ARCH_KIMI_LINEAR
             || arch == LLM_ARCH_BAILINGMOE3
             || arch == LLM_ARCH_KIMI_K3
-            || arch == LLM_ARCH_MISTRAL4) {
+            || arch == LLM_ARCH_MISTRAL4
+            || arch == LLM_ARCH_HY_V4) {
         n_embd = 128;
         n_head = 1;
         n_ff   = 192;
@@ -191,7 +192,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
             || arch == LLM_ARCH_KIMI_LINEAR
             || arch == LLM_ARCH_BAILINGMOE3
             || arch == LLM_ARCH_KIMI_K3
-            || arch == LLM_ARCH_MISTRAL4) {
+            || arch == LLM_ARCH_MISTRAL4
+            || arch == LLM_ARCH_HY_V4) {
         ms.add_kv(LLM_KV_ATTENTION_KEY_LENGTH,       uint32_t(576));
         ms.add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,     uint32_t(512));
         ms.add_kv(LLM_KV_ROPE_DIMENSION_COUNT,       uint32_t(64));
@@ -291,6 +293,22 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS, uint32_t(1));
     ms.add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS, std::vector<uint32_t>({n_embd_head/4, n_embd_head/4, n_embd_head/4, n_embd_head/4}));
 
+    if (arch == LLM_ARCH_HY_V4) {
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_COUNT,     uint32_t(4));
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_EPSILON,   1.0e-6f);
+        ms.add_kv(LLM_KV_HYPER_CONNECTION_MAGNITUDE, 2.0f);
+        ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP,           10.0f);
+        ms.add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE,       1.0f);
+        ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM,        true);
+        // layer 0 must own an indexer, the odd layers share it
+        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);
+    }
+
     if (arch == LLM_ARCH_DEEPSEEK4) {
         ms.add_kv(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,         uint32_t(8));
         ms.add_kv(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,           uint32_t(32));
@@ -468,6 +486,7 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_ERNIE4_5_MOE:
         case LLM_ARCH_HUNYUAN_MOE:
         case LLM_ARCH_HY_V3:
+        case LLM_ARCH_HY_V4:
         case LLM_ARCH_OPENAI_MOE:
         case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_SMALLTHINKER: