diff --git a/python/sglang/srt/layers/rotary_embedding.py b/python/sglang/srt/layers/rotary_embedding.py
index 9e30c6f9ea9..c819543181e 100644
--- a/python/sglang/srt/layers/rotary_embedding.py
+++ b/python/sglang/srt/layers/rotary_embedding.py
@@ -890,6 +890,43 @@ def forward(
         return query_out.type_as(query), key_out.type_as(key)
 
 
+class DynamicNTKAlphaRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with Dynamic NTK scaling.
+
+    Credits to the Reddit users /u/bloc97 and /u/emozilla
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_alpha: float,
+        dtype: torch.dtype,
+    ) -> None:
+        self.scaling_alpha = scaling_alpha
+        super().__init__(
+            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
+        )
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        max_len = self.max_position_embeddings
+        base = self.base * self.scaling_alpha ** (
+            self.rotary_dim / (self.rotary_dim - 2)
+        )
+
+        inv_freq = self._compute_inv_freq(base)
+        t = torch.arange(max_len, dtype=torch.float)
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+
 class MRotaryEmbedding(RotaryEmbedding):
     """Rotary Embedding with Multimodal Sections."""
 
@@ -1234,15 +1271,26 @@ def get_rope(
             )
         elif scaling_type == "dynamic":
             scaling_factor = rope_scaling["factor"]
-            rotary_emb = DynamicNTKScalingRotaryEmbedding(
-                head_size,
-                rotary_dim,
-                max_position,
-                base,
-                is_neox_style,
-                scaling_factor,
-                dtype,
-            )
+            if "alpha" in rope_scaling:
+                rotary_emb = DynamicNTKAlphaRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    rope_scaling["alpha"],
+                    dtype,
+                )
+            else:
+                rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                    head_size,
+                    rotary_dim,
+                    max_position,
+                    base,
+                    is_neox_style,
+                    scaling_factor,
+                    dtype,
+                )
         elif scaling_type == "yarn":
             scaling_factor = rope_scaling["factor"]
             original_max_position = rope_scaling["original_max_position_embeddings"]
diff --git a/python/sglang/srt/models/hunyuan.py b/python/sglang/srt/models/hunyuan.py
new file mode 100644
index 00000000000..00300bed54a
--- /dev/null
+++ b/python/sglang/srt/models/hunyuan.py
@@ -0,0 +1,771 @@
+# coding=utf-8
+# Copyright 2024 The HunYuan team.
+# 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 HunYuan model compatible with HuggingFace weights."""
+import logging
+import re
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from sglang.srt.distributed import (
+    get_pp_group,
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.sampler import Sampler
+from sglang.srt.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE,
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    kv_cache_scales_loader,
+    maybe_remap_kv_scale_name,
+)
+from sglang.srt.utils import add_prefix, is_hip
+
+expert_distribution_recorder = ExpertDistributionRecorder()
+
+
+def _is_moe(config: PretrainedConfig) -> bool:
+    if getattr(config, "num_experts", None) and (
+        (isinstance(config.num_experts, int) and config.num_experts > 1)
+        or (isinstance(config.num_experts, list) and max(config.num_experts) > 1)
+    ):
+        return True
+    else:
+        return False
+
+
+def _get_cla_factor(config: PretrainedConfig) -> int:
+    if not getattr(config, "use_cla", False):
+        return 1
+    return getattr(config, "cla_share_factor", 1)
+
+
+class HunYuanMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        reduce_results: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=hidden_size,
+            output_sizes=[intermediate_size] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=intermediate_size,
+            output_size=hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+            reduce_results=reduce_results,
+        )
+        if hidden_act != "silu":
+            raise ValueError(
+                f"Unsupported activation: {hidden_act}. "
+                "Only silu is supported for now."
+            )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class HunYuanSparseMoeBlock(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_id: int = -1,
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}."
+            )
+
+        # Get layer_id topk if config.moe_topk is a list
+        if isinstance(config.moe_topk, list):
+            assert layer_id >= 0
+            assert len(config.moe_topk) > layer_id
+            top_k = config.moe_topk[layer_id]
+        else:
+            top_k = config.moe_topk
+
+        # If it is moe, moe_intermediate_size is preferred
+        intermediate_size = config.intermediate_size
+        if config.moe_intermediate_size is not None:
+            intermediate_size = (
+                config.moe_intermediate_size
+                if isinstance(config.moe_intermediate_size, int)
+                else config.moe_intermediate_size[layer_id]
+            )
+
+        self.experts = FusedMoE(
+            num_experts=config.num_experts,
+            top_k=top_k,
+            hidden_size=config.hidden_size,
+            intermediate_size=intermediate_size,
+            reduce_results=False,
+            renormalize=True if top_k > 1 else False,
+            quant_config=quant_config,
+        )
+
+        self.gate = ReplicatedLinear(
+            config.hidden_size, config.num_experts, bias=False, quant_config=None
+        )
+        if config.use_mixed_mlp_moe > 0:
+            # Get layer_id num_shared_expert if config.num_shared_expert is a list
+            if isinstance(config.num_shared_expert, list):
+                assert layer_id >= 0
+                assert len(config.num_shared_expert) > layer_id
+                num_shared_expert = config.num_shared_expert[layer_id]
+            else:
+                num_shared_expert = config.num_shared_expert
+
+            self.shared_mlp = HunYuanMLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=config.intermediate_size * num_shared_expert,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+            )
+        else:
+            self.shared_mlp = None
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # NOTE: hidden_states can have either 1D or 2D shape.
+        orig_shape = hidden_states.shape
+        hidden_dim = hidden_states.shape[-1]
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        shared_output = None
+        if self.shared_mlp is not None:
+            shared_output = self.shared_mlp(hidden_states)
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states, router_logits=router_logits
+        )
+        if shared_output is not None:
+            final_hidden_states = final_hidden_states + shared_output
+        if self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
+
+        return final_hidden_states.view(orig_shape)
+
+
+class HunYuanAttention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        attention_type: str = "self",
+        layer_id: int = -1,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        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:
+            # Number of KV heads is greater than TP size, so we partition
+            # the KV heads across multiple tensor parallel GPUs.
+            assert self.total_num_kv_heads % tp_size == 0
+        else:
+            # Number of KV heads is less than TP size, so we replicate
+            # the KV heads across multiple tensor parallel GPUs.
+            assert tp_size % self.total_num_kv_heads == 0
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
+        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.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.use_qk_norm = getattr(config, "use_qk_norm", False)
+        self.attention_type = attention_type
+        self.layer_id = layer_id
+
+        if attention_type == "self":
+            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",
+            )
+        elif attention_type == "cross":
+            self.q_proj = ColumnParallelLinear(
+                hidden_size,
+                hidden_size,
+                bias=bias,
+                quant_config=quant_config,
+                prefix=f"{prefix}.q_proj",
+            )
+        else:
+            raise RuntimeError("Not support attnention type")
+
+        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",
+        )
+
+        is_neox_style = True
+        if quant_config is not None and quant_config.get_name() == "gguf":
+            is_neox_style = False
+
+        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=is_neox_style,
+        )
+        self.attn = RadixAttention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            layer_id=layer_id,
+            prefix=f"{prefix}.attn",
+        )
+
+        if self.use_qk_norm:
+            self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        kv_states: Optional[Tuple[torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        if self.attention_type == "self":
+            qkv, _ = self.qkv_proj(hidden_states)
+            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+            q, k = self.rotary_emb(positions, q, k)
+            ori_k = k
+            if self.use_qk_norm:
+                # q = self.query_layernorm(q.view(-1, self.num_heads, self.head_dim).contiguous())
+                # k = self.key_layernorm(k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
+                q = self.query_layernorm(q.reshape(-1, self.head_dim).contiguous())
+                k = self.key_layernorm(k.reshape(-1, self.head_dim).contiguous())
+        elif self.attention_type == "cross":
+            assert kv_states is not None
+            ori_k, v = kv_states  # use last layer kv,
+            k = ori_k
+            q, _ = self.q_proj(hidden_states)
+            k_tmp = torch.empty_like(k)  # Todo: reduant rotary embedding
+            q, _ = self.rotary_emb(positions, q, k_tmp)
+            if self.use_qk_norm:
+                q = self.query_layernorm(
+                    q.view(-1, self.num_heads, self.head_dim).contiguous()
+                )
+                k = self.key_layernorm(
+                    k.view(-1, self.num_kv_heads, self.head_dim).contiguous()
+                )
+        else:
+            raise RuntimeError("Not support attnention type")
+
+        attn_output = self.attn(q, k, v, forward_batch)
+        output, _ = self.o_proj(attn_output)
+        return output, (ori_k, v)
+
+
+class HunYuanDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        layer_id: int = -1,
+    ) -> None:
+        super().__init__()
+        assert layer_id >= 0
+        self.layer_id = layer_id
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = (
+            config.intermediate_size
+            if isinstance(config.intermediate_size, int)
+            else config.intermediate_size[layer_id]
+        )
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        if rope_scaling is not None and getattr(
+            config, "original_max_position_embeddings", None
+        ):
+            rope_scaling["original_max_position_embeddings"] = (
+                config.original_max_position_embeddings
+            )
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        # Support abacusai/Smaug-72B-v0.1 with attention_bias
+        # Support internlm/internlm-7b with bias
+        attention_bias = getattr(config, "attention_bias", False) or getattr(
+            config, "bias", False
+        )
+        cla_factor = _get_cla_factor(config)
+        attention_type = (
+            "cross" if layer_id >= 0 and layer_id % cla_factor != 0 else "self"
+        )
+        self.self_attn = HunYuanAttention(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=getattr(
+                config, "num_key_value_heads", config.num_attention_heads
+            ),
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=attention_bias,
+            prefix=f"{prefix}.self_attn",
+            attention_type=attention_type,
+            layer_id=layer_id,
+        )
+        if _is_moe(config):
+            self.mlp = HunYuanSparseMoeBlock(
+                config=config,
+                quant_config=quant_config,
+                layer_id=layer_id,
+            )
+        else:
+            self.mlp = HunYuanMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=self.intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                bias=getattr(config, "mlp_bias", False),
+                prefix=f"{prefix}.mlp",
+            )
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        forward_batch: ForwardBatch,
+        residual: Optional[torch.Tensor],
+        kv_states: Optional[Tuple[torch.Tensor]] = None,
+    ) -> 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, ori_kv_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            forward_batch=forward_batch,
+            kv_states=kv_states,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual, ori_kv_states
+
+
+class HunYuanModel(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.org_vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                HunYuanDecoderLayer(
+                    config=config,
+                    layer_id=layer_id,
+                    quant_config=quant_config,
+                    # prefix=prefix
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    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,
+        forward_batch: ForwardBatch,
+        input_embeds: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if input_embeds is not None:
+            hidden_states = input_embeds
+        else:
+            hidden_states = self.get_input_embeddings(input_ids)
+        residual = None
+
+        cla_factor = _get_cla_factor(self.config)
+        prev_kv_states = None
+        for i in range(len(self.layers)):
+            layer = self.layers[i]
+            hidden_states, residual, kv_states = layer(
+                positions,
+                hidden_states,
+                forward_batch,
+                residual,
+                prev_kv_states,
+            )
+
+            if False:  # (i - self.start_layer) % cla_factor == 0:
+                prev_kv_states = kv_states
+            else:
+                prev_kv_states = None
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class HunYuanMoEV1ForCausalLM(nn.Module):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        self.model = HunYuanModel(config, quant_config, prefix="model")
+        self.unpadded_vocab_size = config.vocab_size
+        self.lm_head = ParallelLMHead(
+            config.vocab_size,
+            config.hidden_size,
+            quant_config=quant_config,
+        )
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+
+        logit_scale = getattr(config, "logit_scale", 1.0)
+        self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
+        return self.logits_processor(
+            input_ids, hidden_states, self.lm_head, forward_batch
+        )
+
+    def _split_qkv_weight(self, qkv: torch.Tensor):
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(
+            self.config, "num_key_value_heads", self.config.num_attention_heads
+        )
+        num_key_value_groups = num_attention_heads // num_kv_heads
+        hidden_size = self.config.hidden_size
+        attention_head_dim = self.config.hidden_size // num_attention_heads
+
+        qkv = qkv.reshape(
+            num_kv_heads, num_key_value_groups + 2, attention_head_dim, hidden_size
+        )
+        q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
+        q = q.reshape(-1, hidden_size)
+        k = k.reshape(-1, hidden_size)
+        v = v.reshape(-1, hidden_size)
+        return torch.concat((q, k, v))
+        # return qkv.reshape((num_kv_heads, num_key_value_groups+2 , attention_head_dim, hidden_size)).permute((1,0,2,3)).reshape((-1, hidden_size)),
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        cla_factor = _get_cla_factor(self.config)
+        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),
+        ]
+
+        num_attention_heads = self.config.num_attention_heads
+        num_kv_heads = getattr(
+            self.config, "num_key_value_heads", self.config.num_attention_heads
+        )
+        split_params_mapping = [
+            (".gate_up_proj", ".gate_and_up_proj", 2, [(1, 1), (0, 1)], None),
+            (
+                ".qkv_proj",
+                ".qkv_proj",
+                num_attention_heads + num_kv_heads * 2,
+                [("q", num_attention_heads), ("k", num_kv_heads), ("v", num_kv_heads)],
+                self._split_qkv_weight,
+            ),
+        ]
+
+        if _is_moe(self.config):
+            # Params for weights, fp8 weight scales, fp8 activation scales
+            # (param_name, weight_name, expert_id, shard_id)
+            expert_params_mapping = FusedMoE.make_expert_params_mapping(
+                ckpt_gate_proj_name="gate_proj",
+                ckpt_down_proj_name="down_proj",
+                ckpt_up_proj_name="up_proj",
+                num_experts=self.config.num_experts,
+            )
+        else:
+            expert_params_mapping = {}
+
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "gate_proj_bias" in name:
+                name = name.replace("gate_proj_bias", "gate_proj.bias")
+            if "up_proj_bias" in name:
+                name = name.replace("up_proj_bias", "up_proj.bias")
+            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
+                # Models trained using ColossalAI may include these tensors in
+                # the checkpoint. Skip them.
+                continue
+            # With tie_word_embeddings, we can skip lm_head.weight
+            # The weight might appear unnecessarily in the files if the model is
+            # processed with quantization, LoRA, fine-tuning, etc.
+            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+                continue
+
+            is_found = False
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                if "mlp.experts" in name:
+                    continue
+                # cross layer only have q_proj, skip qkv pack
+                if weight_name == ".q_proj":
+                    match = re.search(r"layers\.\d+", name)
+                    if match:
+                        layer_id = int(match.group(0).split(".")[-1])
+                        if cla_factor > 1 and layer_id % cla_factor != 0:
+                            continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+
+                is_found = True
+                break
+            if is_found:
+                continue
+
+            for param_name, weight_name, den, split_param, func in split_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                assert loaded_weight.shape[0] % den == 0
+                units = loaded_weight.shape[0] // den
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                offset = 0
+                for shard_id, num in split_param:
+                    new_offset = offset + num * units
+                    if func:
+                        weight_loader(
+                            param, func(loaded_weight)[offset:new_offset], shard_id
+                        )
+                    else:
+                        weight_loader(param, loaded_weight[offset:new_offset], shard_id)
+                    offset = new_offset
+
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                for mapping in expert_params_mapping:
+                    param_name, weight_name, expert_id, shard_id = mapping
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    # Skip layers on other devices.
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param,
+                        loaded_weight,
+                        name,
+                        shard_id=shard_id,
+                        expert_id=expert_id,
+                    )
+                    break
+                else:
+                    # Remapping the name of FP8 kv-scale.
+                    name = maybe_remap_kv_scale_name(name, params_dict)
+                    if name is None:
+                        continue
+
+                    if "mlp.gate.wg." in name:
+                        name = name.replace("wg.", "")
+
+                    param = params_dict[name]
+                    weight_loader = getattr(
+                        param, "weight_loader", default_weight_loader
+                    )
+                    weight_loader(param, loaded_weight)
+
+    # If this function is called, it should always initialize KV cache scale
+    # factors (or else raise an exception). Thus, handled exceptions should
+    # make sure to leave KV cache scale factors in a known good (dummy) state
+    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        for layer_idx, scaling_factor in kv_cache_scales_loader(
+            quantization_param_path,
+            tp_rank,
+            tp_size,
+            self.config.num_hidden_layers,
+            self.config.__class__.model_type,
+        ):
+            if not isinstance(self.model.layers[layer_idx], nn.Identity):
+                layer_self_attn = self.model.layers[layer_idx].self_attn
+
+            if is_hip():
+                # The scaling factor convention we are assuming is
+                # quantized_value * scaling_factor ~= true_value
+                # which is consistent with the practice of setting
+                # scaling_factor = tensor_amax / FPtype_max
+                scaling_factor *= 2
+            if hasattr(layer_self_attn, "kv_scale"):
+                layer_self_attn.attn._kv_scale = scaling_factor
+            else:
+                raise RuntimeError(
+                    "Self attention has no KV cache scaling " "factor attribute!"
+                )
+
+
+EntryClass = HunYuanMoEV1ForCausalLM