diff --git a/tests/diffusion/models/t5_encoder/test_t5_encoder_tp.py b/tests/diffusion/models/t5_encoder/test_t5_encoder_tp.py
new file mode 100644
index 000000000..b36acc68a
--- /dev/null
+++ b/tests/diffusion/models/t5_encoder/test_t5_encoder_tp.py
@@ -0,0 +1,161 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from transformers import T5Config
+from vllm.config import DeviceConfig, VllmConfig, set_current_vllm_config
+
+from vllm_omni.diffusion.models.t5_encoder.t5_encoder import (
+    T5EncoderModel,
+    T5SelfAttention,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+_T5_MODULE = "vllm_omni.diffusion.models.t5_encoder.t5_encoder"
+
+SMALL_T5_CONFIG = dict(
+    d_model=64,
+    d_kv=8,
+    d_ff=128,
+    num_heads=8,
+    num_layers=2,
+    vocab_size=256,
+    relative_attention_num_buckets=32,
+    relative_attention_max_distance=128,
+    is_gated_act=True,
+    dense_act_fn="gelu_new",
+    layer_norm_epsilon=1e-6,
+    feed_forward_proj="gated-gelu",
+)
+
+
+@pytest.fixture(scope="module")
+def t5_config() -> T5Config:
+    return T5Config(**SMALL_T5_CONFIG)
+
+
+@pytest.fixture(scope="function", autouse=True)
+def setup_tp_group(monkeypatch, mocker):
+    """Set up TP=2, rank=0, VllmConfig, and mock activation for all tests."""
+    device_config = DeviceConfig(device="cpu")
+
+    # TP world size
+    monkeypatch.setattr("vllm.model_executor.layers.linear.get_tensor_model_parallel_world_size", lambda: 2)
+    monkeypatch.setattr(f"{_T5_MODULE}.get_tensor_model_parallel_world_size", lambda: 2)
+    monkeypatch.setattr(
+        "vllm.model_executor.layers.vocab_parallel_embedding.get_tensor_model_parallel_world_size",
+        lambda: 2,
+    )
+
+    monkeypatch.setattr(f"{_T5_MODULE}.get_tensor_model_parallel_rank", lambda: 0)
+    monkeypatch.setattr(
+        "vllm.model_executor.layers.vocab_parallel_embedding.get_tensor_model_parallel_rank",
+        lambda: 0,
+    )
+
+    # TP group
+    mock_tp_group = mocker.MagicMock()
+    mock_tp_group.world_size = 2
+    mocker.patch("vllm.distributed.parallel_state.get_tp_group", return_value=mock_tp_group)
+
+    monkeypatch.setattr(f"{_T5_MODULE}.get_act_fn", lambda _: torch.nn.GELU())
+
+    with set_current_vllm_config(VllmConfig(device_config=device_config)):
+        yield
+
+
+class TestRelativePositionBiasTPSlicing:
+    """Verify compute_bias slices heads correctly per TP rank."""
+
+    def test_compute_bias_shape(self, t5_config):
+        attn = T5SelfAttention(t5_config, has_relative_attention_bias=True)
+
+        seq_len = 6
+        bias = attn.compute_bias(seq_len, seq_len, device=torch.device("cpu"))
+
+        local_heads = t5_config.num_heads // 2
+        assert bias.shape == (1, local_heads, seq_len, seq_len)
+
+    def test_all_ranks_cover_all_heads(self, t5_config, monkeypatch):
+        seq_len = 4
+
+        biases = []
+        ref_weight = None
+        for rank in range(2):
+            monkeypatch.setattr(f"{_T5_MODULE}.get_tensor_model_parallel_rank", lambda r=rank: r)
+            attn = T5SelfAttention(t5_config, has_relative_attention_bias=True)
+            if rank > 0:
+                attn.relative_attention_bias.weight.data.copy_(ref_weight)
+            else:
+                ref_weight = attn.relative_attention_bias.weight.data.clone()
+            biases.append(attn.compute_bias(seq_len, seq_len, device=torch.device("cpu")))
+
+        full_bias = torch.cat(biases, dim=1)
+        assert full_bias.shape == (1, t5_config.num_heads, seq_len, seq_len)
+
+
+class TestT5EncoderModelWeightLoading:
+    """Test weight loading at the top-level T5EncoderModel."""
+
+    def test_model_instantiation(self, t5_config):
+        model = T5EncoderModel(t5_config, prefix="text_encoder")
+
+        assert model.config is t5_config
+        assert model.encoder is not None
+        assert len(model.encoder.block) == t5_config.num_layers
+
+    def test_embedding_shape(self, t5_config):
+        model = T5EncoderModel(t5_config, prefix="text_encoder")
+
+        assert model.shared.embedding_dim == t5_config.d_model
+
+    def test_embed_input_ids(self, t5_config, monkeypatch):
+        # Verify method and output shape
+        model = T5EncoderModel(t5_config, prefix="text_encoder")
+
+        # Mock all-reduce to be identity (no actual TP communication)
+        monkeypatch.setattr(
+            "vllm.model_executor.layers.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
+            lambda x: x,
+        )
+
+        input_ids = torch.randint(0, t5_config.vocab_size, (2, 8))
+        embeddings = model.embed_input_ids(input_ids)
+
+        assert embeddings.shape == (2, 8, t5_config.d_model)
+
+    def test_qkv_weights_loaded_through_blocks(self):
+        # Verify that HF-style separate Q/K/V weights can be loaded through
+        # the block hierarchy
+        config = T5Config(**{**SMALL_T5_CONFIG, "num_layers": 1})
+        model = T5EncoderModel(config, prefix="text_encoder")
+
+        inner_dim = config.num_heads * config.d_kv
+        prefix = "encoder.block.0.layer.0.SelfAttention."
+
+        loaded = model.load_weights(
+            [
+                (prefix + "q.weight", torch.randn(inner_dim, config.d_model)),
+                (prefix + "k.weight", torch.randn(inner_dim, config.d_model)),
+                (prefix + "v.weight", torch.randn(inner_dim, config.d_model)),
+            ]
+        )
+
+        assert len(loaded) > 0
+        attn = model.encoder.block[0].layer[0].SelfAttention
+        expected_qkv_dim = 3 * (config.num_heads // 2) * config.d_kv
+        assert attn.qkv_proj.weight.shape == (expected_qkv_dim, config.d_model)
+
+
+class TestTPConstraints:
+    """Verify that invalid TP configurations raise clear errors."""
+
+    def test_num_heads_not_divisible_by_tp(self):
+        config = T5Config(**{**SMALL_T5_CONFIG, "num_heads": 7})
+        with pytest.raises(AssertionError, match=r"num_heads.*must be divisible by tp_size"):
+            T5SelfAttention(config)
+
+    def test_num_heads_divisible_by_tp(self, t5_config):
+        attn = T5SelfAttention(t5_config)
+        assert attn.n_heads_per_partition == 4
diff --git a/vllm_omni/diffusion/models/flux/pipeline_flux.py b/vllm_omni/diffusion/models/flux/pipeline_flux.py
index 06af7c95d..b7aa0c7b4 100644
--- a/vllm_omni/diffusion/models/flux/pipeline_flux.py
+++ b/vllm_omni/diffusion/models/flux/pipeline_flux.py
@@ -18,7 +18,7 @@ from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
 )
 from diffusers.utils.torch_utils import randn_tensor
 from torch import nn
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from transformers import AutoConfig, CLIPTextModel, CLIPTokenizer, T5TokenizerFast
 from vllm.model_executor.models.utils import AutoWeightsLoader
 
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
@@ -27,6 +27,7 @@ from vllm_omni.diffusion.distributed.parallel_state import get_classifier_free_g
 from vllm_omni.diffusion.distributed.utils import get_local_device
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
 from vllm_omni.diffusion.models.flux import FluxTransformer2DModel
+from vllm_omni.diffusion.models.t5_encoder import T5EncoderModel
 from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
 from vllm_omni.diffusion.quantization import get_vllm_quant_config_for_layers
 from vllm_omni.diffusion.request import OmniDiffusionRequest
@@ -148,7 +149,14 @@ class FluxPipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
                 revision=None,
                 prefix="transformer.",
                 fall_back_to_pt=True,
-            )
+            ),
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="text_encoder_2",
+                revision=None,
+                prefix="text_encoder_2.",
+                fall_back_to_pt=True,
+            ),
         ]
 
         self.device = get_local_device()
@@ -162,9 +170,8 @@ class FluxPipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
         self.text_encoder = CLIPTextModel.from_pretrained(
             model, subfolder="text_encoder", local_files_only=local_files_only
         ).to(self.device)
-        self.text_encoder_2 = T5EncoderModel.from_pretrained(
-            model, subfolder="text_encoder_2", local_files_only=local_files_only
-        ).to(self.device)
+        t5_config = AutoConfig.from_pretrained(model, subfolder="text_encoder_2", local_files_only=local_files_only)
+        self.text_encoder_2 = T5EncoderModel(t5_config, prefix="text_encoder_2").to(self.device)
         self.vae = AutoencoderKL.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
             self.device
         )
@@ -291,7 +298,7 @@ class FluxPipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
                 f" {max_sequence_length} tokens: {removed_text}"
             )
 
-        prompt_embeds = self.text_encoder_2(text_input_ids.to(self.device), output_hidden_states=False)[0]
+        prompt_embeds = self.text_encoder_2(text_input_ids.to(self.device))[0]
 
         dtype = self.text_encoder_2.dtype
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=self.device)
diff --git a/vllm_omni/diffusion/models/t5_encoder/__init__.py b/vllm_omni/diffusion/models/t5_encoder/__init__.py
new file mode 100644
index 000000000..f2ca52efa
--- /dev/null
+++ b/vllm_omni/diffusion/models/t5_encoder/__init__.py
@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Tensor-parallel T5 encoder model."""
+
+from vllm_omni.diffusion.models.t5_encoder.t5_encoder import T5EncoderModel
+
+__all__ = ["T5EncoderModel"]
diff --git a/vllm_omni/diffusion/models/t5_encoder/t5_encoder.py b/vllm_omni/diffusion/models/t5_encoder/t5_encoder.py
new file mode 100644
index 000000000..7b0e842d0
--- /dev/null
+++ b/vllm_omni/diffusion/models/t5_encoder/t5_encoder.py
@@ -0,0 +1,389 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import math
+from collections.abc import Iterable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import T5Config
+from vllm.distributed import get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+
+class T5SelfAttention(nn.Module):
+    def __init__(
+        self,
+        config: T5Config,
+        has_relative_attention_bias: bool = False,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.d_model = config.d_model
+        self.d_kv = config.d_kv
+        self.n_heads = config.num_heads
+        self.inner_dim = self.n_heads * self.d_kv
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.relative_attention_num_buckets = config.relative_attention_num_buckets
+        self.relative_attention_max_distance = config.relative_attention_max_distance
+
+        tp_size = get_tensor_model_parallel_world_size()
+        assert self.n_heads % tp_size == 0, f"num_heads ({self.n_heads}) must be divisible by tp_size ({tp_size})"
+        self.n_heads_per_partition = self.n_heads // tp_size
+
+        # Fused Q/K/V projection, sharded across heads.
+        # HF has separate q, k, v — we fuse into qkv_proj and use
+        # stacked_params_mapping in load_weights() to load each shard.
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.d_model,
+            head_size=self.d_kv,
+            total_num_heads=self.n_heads,
+            total_num_kv_heads=self.n_heads,  # T5 uses MHA
+            bias=False,
+        )
+
+        # Output projection: all-reduce back to full d_model
+        # Named ``o`` to match HF's ``SelfAttention.o``
+        self.o = RowParallelLinear(
+            self.inner_dim,
+            self.d_model,
+            bias=False,
+            input_is_parallel=True,
+            return_bias=False,
+        )
+
+        if has_relative_attention_bias:
+            # Store full embedding; slice heads per rank in forward
+            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+
+    @staticmethod
+    def _relative_position_bucket(
+        relative_position: torch.Tensor,
+        bidirectional: bool = True,
+        num_buckets: int = 32,
+        max_distance: int = 128,
+    ) -> torch.Tensor:
+        relative_buckets = 0
+        if bidirectional:
+            num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+        max_exact = num_buckets // 2
+        is_small = relative_position < max_exact
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large,
+            torch.full_like(relative_position_if_large, num_buckets - 1),
+        )
+        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length: int, key_length: int, device: torch.device) -> torch.Tensor:
+        """Compute relative position bias, returning only the local head shard."""
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+        relative_position = memory_position - context_position
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,
+            bidirectional=True,
+            num_buckets=self.relative_attention_num_buckets,
+            max_distance=self.relative_attention_max_distance,
+        )
+        # values: (query_length, key_length, num_heads)
+        values = self.relative_attention_bias(relative_position_bucket)
+        # Slice to local heads for this TP rank
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.n_heads_per_partition
+        head_end = head_start + self.n_heads_per_partition
+        values = values[:, :, head_start:head_end]
+        # (1, local_heads, query_length, key_length)
+        values = values.permute(2, 0, 1).unsqueeze(0)
+        return values
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        mask: torch.Tensor | None = None,
+        position_bias: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size, seq_length = hidden_states.shape[:2]
+
+        # Fused QKV projection
+        qkv, _ = self.qkv_proj(hidden_states)
+        q_size = self.n_heads_per_partition * self.d_kv
+        kv_size = self.n_heads_per_partition * self.d_kv
+        query_states, key_states, value_states = qkv.split([q_size, kv_size, kv_size], dim=-1)
+
+        # Reshape: (batch, seq, local_heads, d_kv) -> (batch, local_heads, seq, d_kv)
+        query_states = query_states.view(batch_size, seq_length, self.n_heads_per_partition, self.d_kv).transpose(1, 2)
+        key_states = key_states.view(batch_size, seq_length, self.n_heads_per_partition, self.d_kv).transpose(1, 2)
+        value_states = value_states.view(batch_size, seq_length, self.n_heads_per_partition, self.d_kv).transpose(1, 2)
+
+        # Attention scores: (batch, local_heads, seq, seq)
+        scores = torch.matmul(query_states, key_states.transpose(3, 2))
+
+        if position_bias is None:
+            if self.has_relative_attention_bias:
+                position_bias = self.compute_bias(seq_length, seq_length, device=scores.device)
+            else:
+                position_bias = torch.zeros(
+                    (1, self.n_heads_per_partition, seq_length, seq_length),
+                    device=scores.device,
+                    dtype=scores.dtype,
+                )
+            if mask is not None:
+                position_bias = position_bias + mask
+
+        scores += position_bias
+
+        attn_weights = F.softmax(scores.float(), dim=-1).type_as(scores)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        # (batch, local_heads, seq, d_kv) -> (batch, seq, local_heads * d_kv)
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(batch_size, seq_length, -1)
+
+        attn_output = self.o(attn_output)
+
+        return attn_output, position_bias
+
+
+class T5DenseGatedActDense(nn.Module):
+    def __init__(self, config: T5Config, prefix: str = ""):
+        super().__init__()
+        self.wi = MergedColumnParallelLinear(
+            config.d_model,
+            [config.d_ff, config.d_ff],
+            bias=False,
+            gather_output=False,
+        )
+        self.wo = RowParallelLinear(
+            config.d_ff,
+            config.d_model,
+            bias=False,
+            input_is_parallel=True,
+            return_bias=False,
+        )
+        self.act = get_act_fn(config.dense_act_fn)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # MergedColumnParallelLinear outputs concatenated [gate, up]
+        gate_up, _ = self.wi(hidden_states)
+        gate, up = gate_up.chunk(2, dim=-1)
+        hidden_states = self.act(gate) * up
+        hidden_states = self.wo(hidden_states)
+        return hidden_states
+
+
+class T5DenseActDense(nn.Module):
+    def __init__(self, config: T5Config, prefix: str = ""):
+        super().__init__()
+        self.wi = ColumnParallelLinear(
+            config.d_model,
+            config.d_ff,
+            bias=False,
+            gather_output=False,
+            return_bias=False,
+        )
+        self.wo = RowParallelLinear(
+            config.d_ff,
+            config.d_model,
+            bias=False,
+            input_is_parallel=True,
+            return_bias=False,
+        )
+        self.act = get_act_fn(config.dense_act_fn)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.wi(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.wo(hidden_states)
+        return hidden_states
+
+
+class T5LayerSelfAttention(nn.Module):
+    def __init__(self, config: T5Config, has_relative_attention_bias: bool = False, prefix: str = ""):
+        super().__init__()
+        self.SelfAttention = T5SelfAttention(config, has_relative_attention_bias, prefix=f"{prefix}.SelfAttention")
+        self.layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        mask: torch.Tensor | None = None,
+        position_bias: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        normed = self.layer_norm(hidden_states)
+        attn_output, position_bias = self.SelfAttention(normed, mask=mask, position_bias=position_bias)
+        hidden_states = hidden_states + attn_output
+
+        # Clamp for fp16 stability (matching HF T5)
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+        return hidden_states, position_bias
+
+
+class T5LayerFF(nn.Module):
+    def __init__(self, config: T5Config, prefix: str = ""):
+        super().__init__()
+        if config.is_gated_act:
+            self.DenseReluDense = T5DenseGatedActDense(config, prefix=f"{prefix}.DenseReluDense")
+        else:
+            self.DenseReluDense = T5DenseActDense(config, prefix=f"{prefix}.DenseReluDense")
+        self.layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        normed = self.layer_norm(hidden_states)
+        ff_output = self.DenseReluDense(normed)
+        hidden_states = hidden_states + ff_output
+
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+        return hidden_states
+
+
+class T5Block(nn.Module):
+    def __init__(self, config: T5Config, has_relative_attention_bias: bool = False, prefix: str = ""):
+        super().__init__()
+        self.layer = nn.ModuleList(
+            [
+                T5LayerSelfAttention(config, has_relative_attention_bias, prefix=f"{prefix}.layer.0"),
+                T5LayerFF(config, prefix=f"{prefix}.layer.1"),
+            ]
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        mask: torch.Tensor | None = None,
+        position_bias: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states, position_bias = self.layer[0](hidden_states, mask=mask, position_bias=position_bias)
+        hidden_states = self.layer[1](hidden_states)
+        return hidden_states, position_bias
+
+
+class T5Stack(nn.Module):
+    def __init__(self, config: T5Config, shared: nn.Embedding, prefix: str = ""):
+        super().__init__()
+        self.embed_tokens = shared
+        self.block = nn.ModuleList(
+            [
+                T5Block(config, has_relative_attention_bias=(i == 0), prefix=f"{prefix}.block.{i}")
+                for i in range(config.num_layers)
+            ]
+        )
+        self.final_layer_norm = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+
+        # Build attention mask: (batch, seq) -> (batch, 1, 1, seq)
+        if attention_mask is not None:
+            extended_mask = attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
+            extended_mask = (1.0 - extended_mask) * torch.finfo(hidden_states.dtype).min
+        else:
+            extended_mask = None
+
+        position_bias = None
+        for block in self.block:
+            hidden_states, position_bias = block(
+                hidden_states,
+                mask=extended_mask,
+                position_bias=position_bias,
+            )
+
+        hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class T5EncoderModel(nn.Module):
+    """T5 encoder model applying upstream vLLM layers"""
+
+    def __init__(self, config: T5Config, prefix: str = ""):
+        super().__init__()
+        self.config = config
+        self.shared = VocabParallelEmbedding(config.vocab_size, config.d_model)
+        self.encoder = T5Stack(config, self.shared, prefix=f"{prefix}.encoder")
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(self.parameters()).dtype
+
+    @property
+    def device(self) -> torch.device:
+        return next(self.parameters()).device
+
+    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.shared(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, ...]:
+        hidden_states = self.encoder(input_ids, attention_mask=attention_mask)
+        return (hidden_states,)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q", "q"),
+            ("qkv_proj", "k", "k"),
+            ("qkv_proj", "v", "v"),
+            ("wi", "wi_0", 0),
+            ("wi", "wi_1", 1),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            original_name = name
+            lookup_name = name
+
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if f".{weight_name}." not in name:
+                    continue
+                lookup_name = name.replace(weight_name, param_name)
+                if lookup_name not in params_dict:
+                    continue
+                param = params_dict[lookup_name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+            loaded_params.add(original_name)
+            loaded_params.add(lookup_name)
+
+        return loaded_params