diff --git a/vllm-v0.6.2/vllm/model_executor/models/llama4.py b/vllm-v0.6.2/vllm/model_executor/models/llama4.py
new file mode 100644
index 0000000..4ec2076
--- /dev/null
+++ b/vllm-v0.6.2/vllm/model_executor/models/llama4.py
@@ -0,0 +1,560 @@
+# Copyright 2025 the LLAMA4, Meta Inc., vLLM, and HuggingFace Inc. team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only Llama4 model compatible with HuggingFace weights."""
+import re
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig, VllmConfig
+from vllm.distributed import (get_pp_group,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (QKVParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsPP
+from .llama import LlamaMLP
+from .utils import (AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, make_layers,
+                    maybe_prefix)
+
+logger = init_logger(__name__)
+
+
+def _extract_layer_index(prefix: str) -> int:
+    """Extract layer index from prefix string like 'model.layers.0.self_attn'."""
+    match = re.search(r'layers\.(\d+)', prefix)
+    if match is None:
+        raise ValueError(f"Cannot extract layer index from prefix: {prefix}")
+    return int(match.group(1))
+
+
+class Llama4MoE(nn.Module):
+    """Llama4 Mixture of Experts with shared expert."""
+
+    @staticmethod
+    def custom_routing_function(
+        hidden_states: torch.Tensor,
+        gating_output: torch.Tensor,
+        topk: int,
+        renormalize: bool,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        router_scores, router_indices = torch.topk(
+            gating_output, topk, dim=-1)
+        router_scores = torch.sigmoid(router_scores.float())
+        return (router_scores, router_indices.to(torch.int32))
+
+    def __init__(
+        self,
+        config,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.top_k = getattr(config, "num_experts_per_tok", 1)
+        self.num_local_experts = getattr(config, "num_local_experts", 8)
+        self.hidden_size = getattr(config, "hidden_size", 4096)
+        intermediate_size_moe = getattr(config, "intermediate_size", 8192)
+
+        self.router = ReplicatedLinear(
+            self.hidden_size,
+            self.num_local_experts,
+            bias=False,
+            quant_config=None,
+            prefix=f"{prefix}.router",
+        )
+
+        self.experts = FusedMoE(
+            num_experts=self.num_local_experts,
+            top_k=self.top_k,
+            hidden_size=self.hidden_size,
+            intermediate_size=intermediate_size_moe,
+            reduce_results=False,
+            renormalize=False,
+            quant_config=quant_config,
+            custom_routing_function=Llama4MoE.custom_routing_function,
+            prefix=f"{prefix}.experts",
+        )
+
+        self.shared_expert = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=intermediate_size_moe,
+            hidden_act="silu",
+            quant_config=quant_config,
+            bias=False,
+            prefix=f"{prefix}.shared_expert",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.router(hidden_states)
+        # routed experts
+        routed_out = self.experts(hidden_states, router_logits)
+        # shared expert
+        shared_out = self.shared_expert(hidden_states)
+        # combine and all-reduce
+        experts_out = routed_out + shared_out
+        if self.tp_size > 1:
+            experts_out = tensor_model_parallel_all_reduce(experts_out)
+        return experts_out.view(orig_shape)
+
+
+class Llama4Attention(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        cache_config: Optional[CacheConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = _extract_layer_index(prefix)
+        self.hidden_size = hidden_size
+        self.no_rope_layers = getattr(config, "no_rope_layers", None)
+        self.nope = (self.no_rope_layers is not None
+                     and self.no_rope_layers[self.layer_idx] == 0)
+        self.use_qk_norm = getattr(config, "use_qk_norm", False) and not self.nope
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        if self.total_num_kv_heads >= tp_size:
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        self.head_dim = getattr(config, "head_dim",
+                                self.hidden_size // self.total_num_heads)
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.max_position_embeddings = max_position_embeddings
+
+        # Temperature tuning for NoPE layers
+        self.attn_temperature_tuning = (
+            self.nope and getattr(config, "attn_temperature_tuning", False))
+        self.floor_scale = getattr(config, "floor_scale", 8192.0)
+        self.attn_scale = getattr(config, "attn_scale", 0.1)
+
+        # QK norm
+        rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
+        if self.use_qk_norm:
+            self.qk_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
+            # v0.6.2 RMSNorm doesn't support has_weight=False,
+            # so we set weight to ones and make it non-trainable
+            self.qk_norm.weight.data.fill_(1.0)
+            self.qk_norm.weight.requires_grad = False
+        else:
+            self.qk_norm = None
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+
+        # RoPE (None for NoPE layers)
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if not self.nope:
+            self.rotary_emb = get_rope(
+                self.head_dim,
+                rotary_dim=self.head_dim,
+                max_position=max_position_embeddings,
+                base=rope_theta,
+                rope_scaling=rope_scaling,
+                is_neox_style=True,
+            )
+        else:
+            self.rotary_emb = None
+
+        self.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+        )
+
+    def _get_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
+        floor = torch.floor((positions + 1.0) / self.floor_scale)
+        attn_scale = torch.log(floor + 1.0) * self.attn_scale + 1.0
+        return attn_scale.unsqueeze(-1)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
+                             dim=-1)
+
+        if self.rotary_emb is not None:
+            q, k = self.rotary_emb(positions, q, k)
+
+        if self.qk_norm is not None:
+            q = q.reshape(-1, self.head_dim)
+            q = self.qk_norm(q.float()).reshape(-1, self.q_size).to(q.dtype)
+            k = k.reshape(-1, self.head_dim)
+            k = self.qk_norm(k.float()).reshape(-1, self.kv_size).to(k.dtype)
+
+        if self.attn_temperature_tuning and self.nope:
+            attn_scale = self._get_attn_scale(positions)
+            q = (q * attn_scale).to(q.dtype)
+
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class Llama4DecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layer_idx = _extract_layer_index(prefix)
+        self.hidden_size = getattr(config, "hidden_size", 4096)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+
+        self.self_attn = Llama4Attention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=getattr(config, "num_attention_heads", 32),
+            num_kv_heads=getattr(config, "num_key_value_heads",
+                                 getattr(config, "num_attention_heads", 32)),
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=False,
+            cache_config=cache_config,
+            prefix=f"{prefix}.self_attn",
+        )
+
+        # Interleaved MoE/dense layers
+        interleave_moe_layer_step = getattr(config,
+                                            "interleave_moe_layer_step", 0)
+        is_moe_layer = (interleave_moe_layer_step > 0
+                        and (self.layer_idx + 1)
+                        % interleave_moe_layer_step == 0)
+
+        if is_moe_layer:
+            self.feed_forward = Llama4MoE(
+                config=config,
+                quant_config=quant_config,
+                prefix=f"{prefix}.feed_forward",
+            )
+        else:
+            intermediate_size_mlp = getattr(config, "intermediate_size_mlp",
+                                            getattr(config,
+                                                    "intermediate_size", 8192))
+            self.feed_forward = LlamaMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=intermediate_size_mlp,
+                hidden_act="silu",
+                quant_config=quant_config,
+                bias=False,
+                prefix=f"{prefix}.feed_forward",
+            )
+
+        rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
+        self.input_layernorm = RMSNorm(self.hidden_size, eps=rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(self.hidden_size,
+                                                eps=rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(positions=positions,
+                                       hidden_states=hidden_states,
+                                       kv_cache=kv_cache,
+                                       attn_metadata=attn_metadata)
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.feed_forward(hidden_states)
+        return hidden_states, residual
+
+
+class Llama4Model(nn.Module):
+    """Llama4 model - independent implementation to avoid pad_token_id issue."""
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        config = vllm_config.model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        # Defensive access - Llama4Config may not have pad_token_id
+        self.padding_idx = getattr(config, "pad_token_id", None)
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+
+        if get_pp_group().is_first_rank or (
+                getattr(config, "tie_word_embeddings", False)
+                and get_pp_group().is_last_rank):
+            self.embed_tokens = VocabParallelEmbedding(
+                self.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                quant_config=quant_config,
+            )
+        else:
+            self.embed_tokens = PPMissingLayer()
+
+        self.start_layer, self.end_layer, self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: Llama4DecoderLayer(
+                config=config,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                prefix=prefix),
+            prefix=f"{prefix}.layers",
+        )
+
+        rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
+        if get_pp_group().is_last_rank:
+            self.norm = RMSNorm(config.hidden_size, eps=rms_norm_eps)
+        else:
+            self.norm = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors],
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if get_pp_group().is_first_rank:
+            if inputs_embeds is not None:
+                hidden_states = inputs_embeds
+            else:
+                hidden_states = self.get_input_embeddings(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+
+        for i in range(self.start_layer, self.end_layer):
+            layer = self.layers[i]
+            hidden_states, residual = layer(positions, hidden_states,
+                                            kv_caches[i - self.start_layer],
+                                            attn_metadata, residual)
+
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class Llama4ForCausalLM(nn.Module, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "gate_up_proj": ["gate_proj", "up_proj"],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+        self.config = config
+        self.lora_config = lora_config
+
+        self.model = Llama4Model(vllm_config=vllm_config,
+                                 prefix=maybe_prefix(prefix, "model"))
+
+        if get_pp_group().is_last_rank:
+            self.unpadded_vocab_size = config.vocab_size
+            if lora_config:
+                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+            self.lm_head = ParallelLMHead(
+                self.unpadded_vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+                padding_size=(
+                    DEFAULT_VOCAB_PADDING_SIZE if not lora_config
+                    else lora_config.lora_vocab_padding_size),
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
+            if getattr(config, "tie_word_embeddings", False):
+                self.lm_head = self.lm_head.tie_weights(
+                    self.model.embed_tokens)
+
+            logit_scale = getattr(config, "logit_scale", 1.0)
+            self.logits_processor = LogitsProcessor(
+                self.unpadded_vocab_size,
+                config.vocab_size,
+                logit_scale)
+            self.sampler = get_sampler()
+        else:
+            self.lm_head = PPMissingLayer()
+
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        model_output = self.model(input_ids, positions, kv_caches,
+                                  attn_metadata, intermediate_tensors,
+                                  inputs_embeds)
+        return model_output
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def sample(self, logits: torch.Tensor,
+               sampling_metadata: SamplingMetadata) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
+
+    def permute_qk_weight_for_rotary(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+    ) -> Tuple[str, torch.Tensor]:
+        """Permute Q/K weights for rotary embedding compatibility."""
+        def permute(w: torch.Tensor, n_heads: int):
+            attn_in = getattr(self.config, "head_dim", 128) * n_heads
+            attn_out = getattr(self.config, "hidden_size", 4096)
+            return (w.contiguous()
+                    .view(n_heads, attn_in // n_heads // 2, 2, attn_out)
+                    .transpose(1, 2).reshape(attn_in, attn_out))
+
+        modules = name.split(".")
+        is_weight = modules[-1] == "weight"
+
+        if is_weight:
+            if "k_proj" in modules:
+                loaded_weight = permute(
+                    loaded_weight,
+                    getattr(self.config, "num_key_value_heads", 8))
+            elif "q_proj" in modules:
+                loaded_weight = permute(
+                    loaded_weight,
+                    getattr(self.config, "num_attention_heads", 32))
+
+        return name, loaded_weight
+
+    def load_weights(
+        self, weights: Iterable[Tuple[str, torch.Tensor]],
+    ):
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(
+                ["lm_head."]
+                if getattr(self.config, "tie_word_embeddings", False)
+                else None),
+        )
+        weights = [
+            self.permute_qk_weight_for_rotary(name, loaded_weight)
+            for name, loaded_weight in weights
+        ]
+        loader.load_weights(weights)
diff --git a/vllm-v0.6.2/vllm/model_executor/models/registry.py b/vllm-v0.6.2/vllm/model_executor/models/registry.py
index 75fc5d2..b805401 100644
--- a/vllm-v0.6.2/vllm/model_executor/models/registry.py
+++ b/vllm-v0.6.2/vllm/model_executor/models/registry.py
@@ -65,6 +65,7 @@ _TEXT_GENERATION_MODELS = {
     "InternLM2VEForCausalLM": ("internlm2_ve", "InternLM2VEForCausalLM"),
     "JAISLMHeadModel": ("jais", "JAISLMHeadModel"),
     "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
+    "Llama4ForCausalLM": ("llama4", "Llama4ForCausalLM"),
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/attention/backends/mlu_attn.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/attention/backends/mlu_attn.py
index a75fb3d..82932ff 100644
--- a/vllm-v0.6.2/vllm_mlu/vllm_mlu/attention/backends/mlu_attn.py
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/attention/backends/mlu_attn.py
@@ -582,15 +582,24 @@ def unified_flash_attention_v2(
             else:
                 # unpaged (linear cache) path
                 if use_mla:
-                    # MLA cache 是 2D (total_slots, head_dim)，
-                    # 不能用 reshape_paged_cache（期望 4D），直接索引写入
+                    # MLA: 镜像 paged 路径的处理方式
+                    # key_cache: (num_blocks, 1, block_size, 576)
+                    value_to_cache = None
                     if attn_metadata.prefill_metadata:
                         # MLA prefill cache 已在 forward_prefill 中写入，跳过
                         pass
                     else:
-                        # key: (num_tokens, 1, head_dim) → squeeze → (num_tokens, head_dim)
-                        # key_cache: (total_slots, head_dim)
-                        key_cache[updated_slot_mapping.flatten()] = key.squeeze(1)
+                        if kv_cache_dtype == 'int8':
+                            mlu_ops.quant_to_paged_cache(
+                                key, value_to_cache,
+                                key_cache, value_cache,
+                                key_cache_scale, value_cache_scale,
+                                updated_slot_mapping.flatten())
+                        else:
+                            mlu_ops.reshape_paged_cache(
+                                key, value_to_cache,
+                                key_cache, value_cache,
+                                updated_slot_mapping.flatten())
                 else:
                     # FIXME: After TMO-1496 is completed, remove this code.
                     if key.stride() != value.stride():
diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/config.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/config.py
index 8377054..5f1dc07 100644
--- a/vllm-v0.6.2/vllm_mlu/vllm_mlu/config.py
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/config.py
@@ -37,7 +37,7 @@ def vllm__config__CacheConfig___verify_cache_dtype(self) -> None:
 
 def vllm__config__ModelConfig__get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
     """Returns the number of KV heads per GPU."""
-    if hasattr(self.hf_text_config,"model_type") and self.hf_text_config.model_type == 'deepseek_v2':
+    if hasattr(self.hf_text_config,"model_type") and self.hf_text_config.model_type in ('deepseek_v2', 'deepseek_v3'):
         # feature flag MLA
         return 1
     total_num_kv_heads = self.get_total_num_kv_heads()
@@ -51,7 +51,7 @@ def vllm__config__ModelConfig__get_num_kv_heads(self, parallel_config: "Parallel
 def vllm__config__ModelConfig__get_head_size(self) -> int:
     # TODO remove hard code
     if hasattr(self.hf_text_config, "model_type"
-               ) and self.hf_text_config.model_type == 'deepseek_v2':
+               ) and self.hf_text_config.model_type in ('deepseek_v2', 'deepseek_v3'):
         '''
         =============================
         Modify by vllm_mlu
@@ -109,7 +109,7 @@ def vllm__config__LoRAConfig__verify_with_model_config(self, model_config: Model
 def vllm__config__ModelConfig__is_deepseek_v2(self) -> bool:
     result = hasattr(
         self.hf_text_config,
-        "model_type") and self.hf_text_config.model_type == 'deepseek_v2'
+        "model_type") and self.hf_text_config.model_type in ('deepseek_v2', 'deepseek_v3')
     return result
 
 MluHijackObject.apply_hijack(ModelConfig,
diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/__init__.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/__init__.py
index e38d8ee..979a66f 100755
--- a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/__init__.py
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/__init__.py
@@ -39,3 +39,9 @@ try:
 except ImportError as e:
     import logging
     logging.warning(f"Failed to import mllama hijack: {e}")
+
+try:
+    import vllm_mlu.model_executor.models.llama4
+except ImportError as e:
+    import logging
+    logging.warning(f"Failed to import llama4 hijack: {e}")
diff --git a/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/llama4.py b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/llama4.py
new file mode 100644
index 0000000..eb6bd16
--- /dev/null
+++ b/vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/llama4.py
@@ -0,0 +1,485 @@
+import torch
+import re
+
+from typing import List, Optional, Tuple, Union, Iterable
+from vllm.attention import AttentionMetadata
+from vllm.config import CacheConfig
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm_mlu.model_executor.layers.feed_forward import FeedForward
+from vllm_mlu.mlu_hijack_utils import MluHijackObject
+from vllm.distributed import tensor_model_parallel_all_reduce
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.llama4 import (
+    Llama4Attention, Llama4DecoderLayer, Llama4ForCausalLM,
+    Llama4Model, Llama4MoE)
+from vllm_mlu.model_executor.layers.sparse_moe_mlp import SparseMoeMlp
+from vllm.model_executor.models.utils import is_pp_missing_parameter
+from vllm.sequence import IntermediateTensors
+
+from vllm_mlu.model_executor.models.layer_utils import (
+    decoder_layer_forward_base, decoder_model_forward_base_pp,
+    is_per_tensor_smoothquant, is_per_token_smoothquant,
+    quant_fusion_with_rmsnorm)
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+# ============================================================
+# Llama4MoE MLU replacement: SparseMoeMlp + shared expert
+# ============================================================
+
+class Llama4MoEMlu(SparseMoeMlp):
+    """MLU replacement for Llama4MoE using SparseMoeMlp + shared expert."""
+
+    def __init__(self, config, quant_config=None, prefix=""):
+        num_local_experts = getattr(config, "num_local_experts", 8)
+        top_k = getattr(config, "num_experts_per_tok", 1)
+        hidden_size = getattr(config, "hidden_size", 4096)
+        intermediate_size = getattr(config, "intermediate_size", 8192)
+
+        super().__init__(
+            num_experts=num_local_experts,
+            top_k=top_k,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            up_proj_name="gate_up_proj",
+            is_gated=True,
+            down_proj_name="down_proj",
+            has_bias=False,
+            skip_bias_add=False,
+            renormalize=False,
+            hidden_act="silu",
+            params_dtype=None,
+            quant_config=quant_config,
+            is_use_fused_moe=True,
+        )
+
+        # Llama4 uses sigmoid routing, not softmax
+        # Override topk_softmax to use sigmoid
+        self._use_sigmoid_routing = True
+
+        # Shared expert (independent from routed experts)
+        self.shared_expert = FeedForward(
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            hidden_act="silu",
+            up_proj_name="gate_up_proj",
+            is_gated=True,
+            down_proj_name="down_proj",
+            bias=False,
+            quant_config=quant_config,
+            reduce_results=False,
+            prefix=f"{prefix}.shared_expert",
+        )
+
+    def topk_softmax(self, expert_logits):
+        """Override: Llama4 uses sigmoid routing instead of softmax."""
+        topk_values, topk_indices = torch.topk(
+            expert_logits, self.top_k, dim=-1)
+        topk_values = torch.sigmoid(topk_values.float())
+        return topk_values, topk_indices
+
+    def forward(self, hidden_states, residual=None):
+        orig_shape = hidden_states.shape
+        hidden_states = hidden_states.view(-1, self.hidden_size)
+
+        # Shared expert output
+        shared_out = self.shared_expert(hidden_states)
+
+        # Router logits
+        router_logits, _ = self.gate(hidden_states)
+
+        # Routed experts
+        routed_out = self.forward_experts(hidden_states, router_logits, None)
+
+        # Combine
+        final_out = routed_out + shared_out
+        if self.tp_size > 1:
+            final_out = tensor_model_parallel_all_reduce(final_out)
+
+        return final_out.view(orig_shape)
+
+
+# ============================================================
+# Llama4Attention hijack
+# ============================================================
+
+vllm__llama4__Llama4Attention__init__org = Llama4Attention.__init__
+
+
+def vllm__llama4__Llama4Attention____init__(
+    self,
+    config,
+    hidden_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    max_position_embeddings: int = 8192,
+    quant_config: Optional[QuantizationConfig] = None,
+    bias: bool = False,
+    cache_config: Optional[CacheConfig] = None,
+    prefix: str = "",
+) -> None:
+    vllm__llama4__Llama4Attention__init__org(
+        self, config, hidden_size, num_heads, num_kv_heads,
+        max_position_embeddings, quant_config, bias, cache_config, prefix)
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: save rope_scaling for MLU RoPE dispatch
+    '''
+    self.rope_scaling = getattr(config, "rope_scaling", None)
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+
+
+def vllm__llama4__Llama4Attention__forward(
+    self,
+    positions: torch.Tensor,
+    hidden_states: torch.Tensor,
+    kv_cache: torch.Tensor,
+    attn_metadata: AttentionMetadata,
+    residual: Optional[torch.Tensor] = None,
+    smooth_quant_scale: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    qkv, _ = self.qkv_proj(hidden_states, smooth_quant_scale)
+    q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: MLU RoPE: merge q/k, apply rotary, split back (教训 #3)
+    For NoPE layers (self.rotary_emb is None), skip RoPE entirely.
+    '''
+    if self.rotary_emb is not None:
+        if (self.rope_scaling is not None
+                and self.rope_scaling.get("rope_type") == "longrope"):
+            q, k = self.rotary_emb(positions, q, k)
+        else:
+            qk, _ = qkv.split(
+                [self.q_size + self.kv_size, self.kv_size], dim=-1)
+            self.rotary_emb(
+                positions,
+                qk.view(-1, self.num_heads + self.num_kv_heads,
+                         self.head_dim))
+            q, k = qk.split([self.q_size, self.kv_size], dim=-1)
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+
+    # QK norm (教训 #2: use contiguous + reshape)
+    if self.qk_norm is not None:
+        q = q.contiguous().reshape(-1, self.head_dim)
+        q = (self.qk_norm(q.float())
+             .contiguous().reshape(-1, self.q_size).to(q.dtype))
+        k = k.contiguous().reshape(-1, self.head_dim)
+        k = (self.qk_norm(k.float())
+             .contiguous().reshape(-1, self.kv_size).to(k.dtype))
+
+    # Temperature tuning for NoPE layers
+    if self.attn_temperature_tuning and self.nope:
+        attn_scale = self._get_attn_scale(positions)
+        q = (q * attn_scale).to(q.dtype)
+
+    attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: add residual in o_proj
+    '''
+    output, _ = self.o_proj(attn_output, residual)
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+    return output
+
+
+# ============================================================
+# Llama4DecoderLayer hijack
+# ============================================================
+
+def vllm__llama4__Llama4DecoderLayer____init__(
+    self,
+    config,
+    cache_config: Optional[CacheConfig] = None,
+    quant_config: Optional[QuantizationConfig] = None,
+    prefix: str = "",
+) -> None:
+    super(Llama4DecoderLayer, self).__init__()
+    from vllm.model_executor.models.llama4 import (
+        _extract_layer_index, Llama4Attention)
+
+    self.layer_idx = _extract_layer_index(prefix)
+    self.hidden_size = getattr(config, "hidden_size", 4096)
+    max_position_embeddings = getattr(
+        config, "max_position_embeddings", 8192)
+
+    self.self_attn = Llama4Attention(
+        config=config,
+        hidden_size=self.hidden_size,
+        num_heads=getattr(config, "num_attention_heads", 32),
+        num_kv_heads=getattr(config, "num_key_value_heads",
+                             getattr(config, "num_attention_heads", 32)),
+        max_position_embeddings=max_position_embeddings,
+        quant_config=quant_config,
+        bias=False,
+        cache_config=cache_config,
+        prefix=f"{prefix}.self_attn",
+    )
+
+    interleave_moe_layer_step = getattr(
+        config, "interleave_moe_layer_step", 0)
+    is_moe_layer = (interleave_moe_layer_step > 0
+                    and (self.layer_idx + 1)
+                    % interleave_moe_layer_step == 0)
+
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: Replace MoE with Llama4MoEMlu (SparseMoeMlp + shared expert),
+            Replace dense MLP with FeedForward.
+    '''
+    if is_moe_layer:
+        self.feed_forward = Llama4MoEMlu(
+            config=config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.feed_forward",
+        )
+    else:
+        intermediate_size_mlp = getattr(
+            config, "intermediate_size_mlp",
+            getattr(config, "intermediate_size", 8192))
+        self.feed_forward = FeedForward(
+            hidden_size=self.hidden_size,
+            intermediate_size=intermediate_size_mlp,
+            hidden_act="silu",
+            up_proj_name="gate_up_proj",
+            is_gated=True,
+            down_proj_name="down_proj",
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.feed_forward",
+        )
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+
+    rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
+    self.input_layernorm = RMSNorm(self.hidden_size, eps=rms_norm_eps)
+    self.post_attention_layernorm = RMSNorm(
+        self.hidden_size, eps=rms_norm_eps)
+
+    self.is_per_tesnor_sq_perf_cases = is_per_tensor_smoothquant(
+        quant_config)
+    self.is_per_token_sq_perf_cases = is_per_token_smoothquant(
+        quant_config)
+    if self.is_per_tesnor_sq_perf_cases or self.is_per_token_sq_perf_cases:
+        self.self_attn.qkv_proj.quant_method.skip_quant_input = True
+        self.quant_fusion_attn_layernorm = None
+
+
+def vllm__llama4__Llama4DecoderLayer__forward(
+    self,
+    positions: torch.Tensor,
+    hidden_states: torch.Tensor,
+    kv_cache: torch.Tensor,
+    attn_metadata: AttentionMetadata,
+) -> torch.Tensor:
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: use decoder_layer_forward_base with residual-in-matmul
+            and optional quant fusion.
+    '''
+    attn_layernorm = self.input_layernorm
+    if self.is_per_tesnor_sq_perf_cases or self.is_per_token_sq_perf_cases:
+        if self.quant_fusion_attn_layernorm is None:
+            if self.is_per_token_sq_perf_cases:
+                attn_quant_scale = self.self_attn.qkv_proj.smooth
+            else:
+                attn_quant_scale = self.self_attn.qkv_proj.scale_to_int
+            self.quant_fusion_attn_layernorm = quant_fusion_with_rmsnorm(
+                self.input_layernorm, attn_quant_scale,
+                dynamic_quant=self.is_per_token_sq_perf_cases)
+        attn_layernorm = self.quant_fusion_attn_layernorm
+
+    return decoder_layer_forward_base(
+        positions, hidden_states, kv_cache, attn_metadata,
+        attn_layernorm,
+        self.self_attn,
+        self.post_attention_layernorm,
+        self.feed_forward,
+        input_norm_fuse_en=self.is_per_token_sq_perf_cases)
+
+
+# ============================================================
+# Llama4Model hijack
+# ============================================================
+
+def vllm__llama4__Llama4Model__forward(
+    self,
+    input_ids: Optional[torch.Tensor],
+    positions: torch.Tensor,
+    kv_caches: List[torch.Tensor],
+    attn_metadata: AttentionMetadata,
+    intermediate_tensors: Optional[IntermediateTensors],
+    inputs_embeds: Optional[torch.Tensor] = None,
+) -> Union[torch.Tensor, IntermediateTensors]:
+    return decoder_model_forward_base_pp(
+        input_ids, positions, kv_caches, attn_metadata,
+        intermediate_tensors,
+        self.layers, self.start_layer, self.end_layer,
+        self.get_input_embeddings,
+        self.norm,
+        inputs_embeds)
+
+
+# ============================================================
+# Llama4ForCausalLM load_weights hijack
+# ============================================================
+
+def vllm__llama4__Llama4ForCausalLM__load_weights(
+    self,
+    weights: Iterable[Tuple[str, torch.Tensor]],
+):
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: pack params for SparseMoeMlp (MoE layers)
+    '''
+    for name, m in self.model.named_modules():
+        if isinstance(m, SparseMoeMlp):
+            m.pack_params()
+
+    start_expert_id = 0
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+
+    stacked_params_mapping = [
+        # (param_name, shard_name, shard_id)
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+        ("gate_up_proj", "gate_proj", 0),
+        ("gate_up_proj", "up_proj", 1),
+    ]
+
+    params_dict = dict(self.named_parameters())
+    for name, loaded_weight in weights:
+        if "rotary_emb.inv_freq" in name:
+            continue
+
+        # Permute Q/K weights for rotary embedding
+        name, loaded_weight = self.permute_qk_weight_for_rotary(
+            name, loaded_weight)
+
+        '''
+        =============================
+        Modify by vllm_mlu
+        =============================
+        @brief: remap expert_id for distributed inference
+        '''
+        if (start_expert_id > 0
+                and "feed_forward.experts." in name):
+            match = re.search(r'experts\.\d+', name)
+            if match:
+                expert_str = match.group(0)
+                expert_id = int(expert_str.split(".")[1])
+                named_expert_id = expert_id - start_expert_id
+                name = name.replace(
+                    f"experts.{expert_id}",
+                    f"experts.{named_expert_id}")
+        '''
+        ==================
+        End of MLU Hijack
+        ==================
+        '''
+
+        for (param_name, weight_name, shard_id) in stacked_params_mapping:
+            if weight_name not in name:
+                continue
+            name = name.replace(weight_name, param_name)
+            if ((name.endswith(".bias") or name.endswith("_bias"))
+                    and name not in params_dict):
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+            # Skip experts not assigned to this worker
+            if ("feed_forward.experts." in name
+                    and name not in params_dict):
+                continue
+            param = params_dict[name]
+            weight_loader = param.weight_loader
+            weight_loader(param, loaded_weight, shard_id)
+            break
+        else:
+            if ((name.endswith(".bias") or name.endswith("_bias"))
+                    and name not in params_dict):
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+            name = maybe_remap_kv_scale_name(name, params_dict)
+            if name is None:
+                continue
+            # Skip experts not assigned to this worker
+            if ("feed_forward.experts." in name
+                    and name not in params_dict):
+                continue
+            if name not in params_dict:
+                logger.warning(
+                    "Skipping weight %s not present in the model", name)
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(
+                param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+# ============================================================
+# Apply all hijacks
+# ============================================================
+
+MluHijackObject.apply_hijack(
+    Llama4Attention,
+    Llama4Attention.__init__,
+    vllm__llama4__Llama4Attention____init__)
+MluHijackObject.apply_hijack(
+    Llama4Attention,
+    Llama4Attention.forward,
+    vllm__llama4__Llama4Attention__forward)
+MluHijackObject.apply_hijack(
+    Llama4DecoderLayer,
+    Llama4DecoderLayer.__init__,
+    vllm__llama4__Llama4DecoderLayer____init__)
+MluHijackObject.apply_hijack(
+    Llama4DecoderLayer,
+    Llama4DecoderLayer.forward,
+    vllm__llama4__Llama4DecoderLayer__forward)
+MluHijackObject.apply_hijack(
+    Llama4Model,
+    Llama4Model.forward,
+    vllm__llama4__Llama4Model__forward)
+MluHijackObject.apply_hijack(
+    Llama4ForCausalLM,
+    Llama4ForCausalLM.load_weights,
+    vllm__llama4__Llama4ForCausalLM__load_weights)