diff --git "a/docs/source/Instruction/Megatron-SWIFT\350\256\255\347\273\203.md" "b/docs/source/Instruction/Megatron-SWIFT\350\256\255\347\273\203.md"
index 8b54ecb2a..30d85a3f8 100644
--- "a/docs/source/Instruction/Megatron-SWIFT\350\256\255\347\273\203.md"
+++ "b/docs/source/Instruction/Megatron-SWIFT\350\256\255\347\273\203.md"
@@ -16,6 +16,9 @@ pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable
 git clone https://github.com/NVIDIA/apex
 cd apex
 pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+
+# megatron-core
+pip install git+https://github.com/NVIDIA/Megatron-LM.git@core_r0.11.0
 ```
 
 或者你也可以使用镜像：
@@ -24,7 +27,7 @@ modelscope-registry.cn-hangzhou.cr.aliyuncs.com/modelscope-repo/modelscope:ubunt
 modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.4.0-py311-torch2.6.0-vllm0.8.3-modelscope1.25.0-swift3.3.0.post1
 ```
 
-依赖库Megatron-LM将会由swift进行git clone并安装，不需要用户手动安装。你也可以通过环境变量`MEGATRON_LM_PATH`指向已经下载好的repo路径（断网环境，[core_r0.11.0分支](https://github.com/NVIDIA/Megatron-LM/tree/core_r0.11.0)）。
+依赖库Megatron-LM中的训练模块将由swift进行git clone并安装。你也可以通过环境变量`MEGATRON_LM_PATH`指向已经下载好的repo路径（断网环境，[core_r0.11.0分支](https://github.com/NVIDIA/Megatron-LM/tree/core_r0.11.0)）。
 
 
 ## 快速入门案例
@@ -104,13 +107,22 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 
 ## Benchmark
 
-使用`megatron sft`和`swift sft`在单机八卡A800环境下进行14B模型全参数训练的速度对比如下，对应脚本参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/train/megatron/benchmark)。
+使用`megatron sft`和`swift sft`在单机八卡A800环境下进行Dense/MoE模型全参数训练的速度对比如下，对应脚本参考[这里](https://github.com/modelscope/ms-swift/tree/main/examples/train/megatron/benchmark)。
+
+**Dense** Qwen2.5-14B:
 
 |          | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 |
 | -------- | ----------- | ---------- | ---------- |
 | 训练速度 |      9.04s/it       |  10.32s/it   | 10.56s/it |
 | 显存占用 | 8\*64GB     |  8\*80GB   | 8\*58GB |
 
+**MoE** Qwen1.5-MoE-A2.7B:
+
+|          | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 |
+| -------- | ----------- | ---------- | ---------- |
+| 训练速度 |      2.93s/it       |  6.02s/it   | 24.30s/it |
+| 显存占用 | 8\*66GB     |  8\*72GB   | 8\*50GB |
+
 
 ## 命令行参数
 
@@ -187,8 +199,8 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 - overlap_param_gather: 启用分布式优化器中参数all-gather的重叠（降低DP通信耗时）。默认为False。
 - distributed_timeout_minutes: torch.distributed的timeout时间（单位为分钟），默认为60分钟。
 
-**日志参数**
-- log_params_norm: 记录参数的norm。默认为True。
+**日志参数**:
+- log_params_norm: 记录参数的norm。默认为False。
 - log_throughput: 记录每个GPU的吞吐量。默认为True。
   - 注意：在非packing情况下，log_throughput并不准确，因为`seq_length`并不等于真实序列长度。
 - tensorboard_log_interval: 记录到tensorboard的间隔（steps），默认为1。
@@ -199,11 +211,11 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 - log_memory_to_tensorboard: 将内存日志写入tensorboard。默认为True。
 - logging_leval: 日志级别。默认为None。
 
-**评估参数**
+**评估参数**:
 - 🔥eval_iters: 评估的迭代次数，默认为100。
 - 🔥eval_interval: 评估的间隔（steps），默认为None，即设置为save_interval。
 
-**混合精度参数**
+**混合精度参数**:
 - fp16: fp16模式。默认为None，会根据模型的torch_dtype进行设置。torch_dtype默认读取config.json。
 - bf16: bf16模式。默认为None，会根据模型的torch_dtype进行设置。
 - apply_query_key_layer_scaling: 将`Q * K^T` 缩放为 `1 / 层数`（例如：第layer_num层则除以layer_num）。这对fp16训练很有帮助。默认为None，即若使用`--fp16`，则设置为True。
@@ -228,9 +240,29 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 - add_qkv_bias: 仅在QKV的linear中增加bias，默认为True。
 - attention_dropout: 默认为0.。
 - hidden_dropout: 默认为0.。
+- kv_channels: 默认为None，设置为`args.hidden_size // args.num_attention_heads`。
+- qk_layernorm: 是否对Q和K进行层归一化。
 - transformer_impl: 使用哪种transformer实现，可选项为'local'和'transformer_engine'。默认为transformer_engine。
 - padded_vocab_size: 完整词表大小，默认为None。
 - rope_scaling: rope_scaling相关参数，默认为None。格式参考[llama3.1 config.json](https://modelscope.cn/models/LLM-Research/Meta-Llama-3.1-8B-Instruct/file/view/master?fileName=config.json&status=1)，传入json字符串。
+- model_type: Huggingface模型权重中config.json中的model_type。
+
+
+**MoE参数**:
+- num_experts: MoE的专家数，默认为None。自动从config.json读取。
+- moe_ffn_hidden_siz: 每个专家的前馈网络（ffn）的隐藏层大小。默认为None，设置为ffn_hidden_size。自动从config.json读取。
+- moe_shared_expert_intermediate_size: 共享专家的总FFN隐藏层大小。如果有多个共享专家，它应等于 `num_shared_experts * ffn_size_of_each_shared_expert`。 默认为None。自动从config.json读取。
+- moe_router_topk: 每个token路由到的专家数量。默认为None。自动从config.json读取。
+- moe_router_pre_softmax: 为MoE启用预softmax路由，这意味着softmax会在top-k选择之前进行。默认为None。自动从config.json读取。
+- moe_aux_loss_coeff: 辅助损失的缩放系数：建议的初始值为 1e-2。默认为None。自动从config.json读取。
+- expert_model_parallel_size: 专家并行数，默认为1。
+- moe_token_dispatcher_type: 要使用的token分发器类型。可选选项包括 'allgather'、'alltoall' 和 'alltoall_seq'。默认值为 'alltoall'。
+- moe_grouped_gemm: 当每个rank包含多个专家时，通过在多个流中启动多个本地 GEMM 内核，利用 TransformerEngine中的GroupedLinear提高利用率和性能。默认为False。
+- moe_router_load_balancing_type: 确定路由器的负载均衡策略。可选项为"aux_loss"、"seq_aux_loss"、"sinkhorn"、"none"。默认值为 "aux_loss"。
+- moe_z_loss_coeff: z-loss 的缩放系数。默认为None。
+- moe_expert_capacity_factor: 每个专家的容量因子，None表示不会丢弃任何token。默认为None。
+- moe_shared_expert_overlap: 启用共享专家计算与调度器通信之间的重叠。如果不启用此选项，共享专家将在路由专家之后执行。仅在设置了`moe_shared_expert_intermediate_size`时有效。默认为False。
+
 
 ### Megatron训练参数
 
@@ -240,3 +272,5 @@ Megatron训练参数继承自Megatron参数和基本参数。基本参数的内
 - 🔥packing: 是否使用序列packing，默认为False。
 - 🔥streaming: 流式读取并处理数据集，默认False。通常在处理大型数据集时，设置为True。更多流式的参数查看命令行参数文档。
 - lazy_tokenize: 默认为False。若该参数设置为False，则在训练之前对所有的数据集样本进行tokenize（这可以避免在训练中出现报错）；设置为True，则在训练中对数据集进行tokenize（这可以节约内存）。
+- dataloader_persistent_workers: 透传入dataloader的参数，默认为True。
+- dataloader_prefetch_factor: 透传入dataloader的参数，默认为10。
diff --git "a/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md" "b/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md"
index d779d31cd..2da5acafe 100644
--- "a/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md"
+++ "b/docs/source/Instruction/\345\221\275\344\273\244\350\241\214\345\217\202\346\225\260.md"
@@ -48,6 +48,7 @@
   - 注意：CPT/SFT的随机包括两个部分：数据集的随机，由`dataset_shuffle`控制；train_dataloader中的随机，由`train_dataloader_shuffle`控制。
 - val_dataset_shuffle: 是否对val_dataset进行随机操作。默认为False。
 - 🔥streaming: 流式读取并处理数据集，默认False。通常在处理大型数据集时，设置为True
+  - 注意：需要额外设置`--max_steps`，因为流式数据集无法获得其长度
 - interleave_prob: 默认值为 None。在组合多个数据集时，默认使用 `concatenate_datasets` 函数；如果设置了该参数，则会使用 `interleave_datasets` 函数。该参数通常用于流式数据集的组合，并会作为参数传入 `interleave_datasets` 函数中
 - stopping_strategy: 可选为"first_exhausted", "all_exhausted"，默认为"first_exhausted"。传入interleave_datasets函数中
 - shuffle_buffer_size: 该参数用于指定流式数据集的随机buffer大小，默认为1000
diff --git "a/docs/source/Instruction/\346\224\257\346\214\201\347\232\204\346\250\241\345\236\213\345\222\214\346\225\260\346\215\256\351\233\206.md" "b/docs/source/Instruction/\346\224\257\346\214\201\347\232\204\346\250\241\345\236\213\345\222\214\346\225\260\346\215\256\351\233\206.md"
index d7558d330..28ccb11c3 100644
--- "a/docs/source/Instruction/\346\224\257\346\214\201\347\232\204\346\250\241\345\236\213\345\222\214\346\225\260\346\215\256\351\233\206.md"
+++ "b/docs/source/Instruction/\346\224\257\346\214\201\347\232\204\346\250\241\345\236\213\345\222\214\346\225\260\346\215\256\351\233\206.md"
@@ -173,11 +173,11 @@
 |[Qwen/Qwen2.5-Math-1.5B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-1.5B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B)|
 |[Qwen/Qwen2.5-Math-7B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-7B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B)|
 |[Qwen/Qwen2.5-Math-72B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-72B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B)|
-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat)|
-|[Qwen/Qwen1.5-MoE-A2.7B](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B)|
+|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat)|
+|[Qwen/Qwen1.5-MoE-A2.7B](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B)|
 |[Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4)|
-|[Qwen/Qwen2-57B-A14B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)|
-|[Qwen/Qwen2-57B-A14B](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B](https://huggingface.co/Qwen/Qwen2-57B-A14B)|
+|[Qwen/Qwen2-57B-A14B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)|
+|[Qwen/Qwen2-57B-A14B](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen2-57B-A14B](https://huggingface.co/Qwen/Qwen2-57B-A14B)|
 |[Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4)|
 |[Qwen/QwQ-32B-Preview](https://modelscope.cn/models/Qwen/QwQ-32B-Preview)|qwq_preview|qwq_preview|transformers>=4.37|✔|-|[Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)|
 |[Qwen/QwQ-32B](https://modelscope.cn/models/Qwen/QwQ-32B)|qwq|qwq|transformers>=4.37|✔|-|[Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)|
diff --git a/docs/source_en/Instruction/Command-line-parameters.md b/docs/source_en/Instruction/Command-line-parameters.md
index 3b28807c8..f5238c153 100644
--- a/docs/source_en/Instruction/Command-line-parameters.md
+++ b/docs/source_en/Instruction/Command-line-parameters.md
@@ -49,6 +49,7 @@ Hints:
   - Note: The shuffling in CPT/SFT consists of two parts: dataset shuffling, controlled by `dataset_shuffle`; and shuffling in the train_dataloader, controlled by `train_dataloader_shuffle`.
 - val_dataset_shuffle: Whether to perform shuffling on the val_dataset. Default is False.
 - 🔥streaming: Stream reading and processing of the dataset, default is False. It is typically set to True when handling large datasets.
+  - Note: It is necessary to set `--max_steps` additionally, as the length of the streaming dataset cannot be obtained.
 - interleave_prob: Defaults to None. When combining multiple datasets, the `concatenate_datasets` function is used by default. If this parameter is set, the `interleave_datasets` function will be used instead. This parameter is typically used when combining streaming datasets and is passed to the `interleave_datasets` function.
 - stopping_strategy: Can be either "first_exhausted" or "all_exhausted", with the default being "first_exhausted". This parameter is passed to the `interleave_datasets` function.
 - shuffle_buffer_size: This parameter is used to specify the shuffle buffer size for streaming datasets. Defaults to 1000.
diff --git a/docs/source_en/Instruction/Megatron-SWIFT-Training.md b/docs/source_en/Instruction/Megatron-SWIFT-Training.md
index 6195281cf..0772f821d 100644
--- a/docs/source_en/Instruction/Megatron-SWIFT-Training.md
+++ b/docs/source_en/Instruction/Megatron-SWIFT-Training.md
@@ -17,6 +17,9 @@ pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable
 git clone https://github.com/NVIDIA/apex
 cd apex
 pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+
+# megatron-core
+pip install git+https://github.com/NVIDIA/Megatron-LM.git@core_r0.11.0
 ```
 
 Alternatively, you can also use the image:
@@ -25,7 +28,7 @@ modelscope-registry.cn-hangzhou.cr.aliyuncs.com/modelscope-repo/modelscope:ubunt
 modelscope-registry.us-west-1.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.4.0-py311-torch2.6.0-vllm0.8.3-modelscope1.25.0-swift3.3.0.post1
 ```
 
-The dependency library Megatron-LM will be git cloned and installed by swift, no manual installation by the user is required. You can also use the environment variable `MEGATRON_LM_PATH` to point to the already downloaded repo path (for offline environments, use the [core_r0.11.0 branch](https://github.com/NVIDIA/Megatron-LM/tree/core_r0.11.0)).
+The training module in the dependent library Megatron-LM will be cloned and installed by swift via `git clone`. Alternatively, you can use the environment variable `MEGATRON_LM_PATH` to point to the path of an already downloaded repository (in offline environments, use the [core_r0.11.0 branch](https://github.com/NVIDIA/Megatron-LM/tree/core_r0.11.0)).
 
 
 ## Quick Start Example
@@ -109,11 +112,22 @@ I am a language model developed by swift, you can call me swift-robot. How can I
 - For pretraining, you can use `megatron pt` instead of `megatron sft`, which will use a generative template for training.
 
 ## Benchmark
+The speed comparison of full-parameter training for Dense/MoE models using `megatron sft` and `swift sft` on a single machine with eight A800 GPUs is shown below. The corresponding scripts can be found [here](https://github.com/modelscope/ms-swift/tree/main/examples/train/megatron/benchmark).
+
+**Dense** Qwen2.5-14B:
+
+
+|                  | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 |
+| ---------------- | ----------- | --------------- | --------------- |
+| Training Speed   | 9.04s/it    | 10.32s/it       | 10.56s/it       |
+| GPU Memory Usage | 8\*64GB      | 8\*80GB          | 8\*58GB          |
 
-|                  | Megatron-LM      | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 |
-| ---------------- | --------------- | --------------- | --------- |
-| Training Speed   | 9.04s/it        | 10.32s/it       | 10.56s/it |
-| GPU Memory Usage | 8\*64GB          | 8\*80GB          | 8\*58GB    |
+**MoE** Qwen1.5-MoE-A2.7B:
+
+|                  | Megatron-LM | Deepspeed-ZeRO2 | Deepspeed-ZeRO3 |
+| ---------------- | ----------- | --------------- | --------------- |
+| Training Speed   | 2.93s/it    | 6.02s/it        | 24.30s/it       |
+| GPU Memory Usage | 8\*66GB      | 8\*72GB          | 8\*50GB          |
 
 ## Command Line Arguments
 
@@ -193,9 +207,9 @@ seq_length: Defaults to None, meaning it is set to `max_length`. To restrict the
 - overlap_param_gather: Overlap all-gather of parameters in the distributed optimizer (to reduce DP communication time). Default is False.
 - distributed_timeout_minutes: Timeout duration for torch.distributed (in minutes), default is 60 minutes.
 
-**Logging Parameters**
+**Logging Parameters**:
 
-- log_params_norm: Logs the norm of parameters. Default is True.
+- log_params_norm: Logs the norm of parameters. Default is False.
 - log_throughput: Logs throughput per GPU. Default is True.
   - Note: In non-packing scenarios, log_throughput is not accurate because `seq_length` does not equal the actual sequence length.
 - tensorboard_log_interval: Interval (steps) for logging to TensorBoard, default is 1.
@@ -206,12 +220,12 @@ seq_length: Defaults to None, meaning it is set to `max_length`. To restrict the
 - log_memory_to_tensorboard: Writes memory logs to TensorBoard. Default is True.
 - logging_level: Logging level. Default is None.
 
-**Evaluation Parameters**
+**Evaluation Parameters**:
 
 - 🔥eval_iters: Number of evaluation iterations, default is 100.
 - 🔥eval_interval: Evaluation interval (steps), default is None, meaning it will be set to save_interval.
 
-**Mixed Precision Parameters**
+**Mixed Precision Parameters**:
 
 - fp16: FP16 mode. The default is None, and it will be set according to the model's torch_dtype. The torch_dtype is read from the config.json by default.
 - bf16: BF16 mode. The default is None, and it will be set according to the model's torch_dtype.
@@ -238,9 +252,29 @@ seq_length: Defaults to None, meaning it is set to `max_length`. To restrict the
 - add_qkv_bias: Adds bias only to QKV linear layers. Default is True.
 - attention_dropout: Default is 0.
 - hidden_dropout: Default is 0.
+- kv_channels: Defaults to None, set to `args.hidden_size // args.num_attention_heads`.
+- qk_layernorm: Whether to apply layer normalization to Q and K.
 - transformer_impl: Which transformer implementation to use, options are 'local' and 'transformer_engine'. Default is transformer_engine.
 - padded_vocab_size: Full vocabulary size, default is None.
 - rope_scaling: Related parameters for rope_scaling, default is None. Refer to the format in [llama3.1 config.json](https://modelscope.cn/models/LLM-Research/Meta-Llama-3.1-8B-Instruct/file/view/master?fileName=config.json&status=1). Pass the value as a JSON string.
+- model_type: The model_type in the config.json of the Huggingface model weights.
+
+
+**MoE Parameters**:
+
+- num_experts: The number of experts in MoE, default is None. Automatically read from config.json.
+- moe_ffn_hidden_size: The hidden layer size of the feed-forward network (ffn) for each expert. Default is None, set to ffn_hidden_size. Automatically read from config.json.
+- moe_shared_expert_intermediate_size: The total FFN hidden layer size for shared experts. If there are multiple shared experts, it should equal `num_shared_experts * ffn_size_of_each_shared_expert`. Default is None. Automatically read from config.json.
+- moe_router_topk: The number of experts each token is routed to. Default is None. Automatically read from config.json.
+- moe_router_pre_softmax: Enable pre-softmax routing for MoE, meaning that softmax will be applied before top-k selection. Default is None. Automatically read from config.json.
+- moe_aux_loss_coeff: Scaling coefficient for the auxiliary loss: the recommended initial value is 1e-2. Default is None. Automatically read from config.json.
+- expert_model_parallel_size: The degree of expert parallelism, default is 1.
+- moe_token_dispatcher_type: The type of token dispatcher to use. Options include 'allgather', 'alltoall', and 'alltoall_seq'. Default is 'alltoall'.
+- moe_grouped_gemm: When each rank contains multiple experts, improve utilization and performance by launching multiple local GEMM kernels across multiple streams using GroupedLinear in TransformerEngine. Default is False.
+- moe_router_load_balancing_type: Determines the load balancing strategy for the router. Options are "aux_loss", "seq_aux_loss", "sinkhorn", "none". Default is "aux_loss".
+- moe_z_loss_coeff: Scaling coefficient for z-loss. Default is None.
+- moe_expert_capacity_factor: Capacity factor for each expert, None means no tokens will be dropped. Default is None.
+- moe_shared_expert_overlap: Enable overlapping of shared expert computation with scheduler communication. If this option is not enabled, shared experts will execute after the routing experts. Only effective when `moe_shared_expert_intermediate_size` is set. Default is False.
 
 ### Megatron Training Parameters
 
@@ -250,3 +284,5 @@ Megatron training parameters inherit from Megatron parameters and basic paramete
 - 🔥packing: Whether to use sequence packing, defaults to False.
 - 🔥streaming: Stream reading and processing of the dataset, default is False. It is typically set to True when handling large datasets. For more information on streaming parameters, refer to the command-line parameters documentation.
 - lazy_tokenize: Default is False. If this parameter is set to False, all dataset samples are tokenized before training (this avoids errors during training); if set to True, tokenization occurs during training (this saves memory).
+- dataloader_persistent_workers: A parameter passed directly to the dataloader, with a default value of True.
+- dataloader_prefetch_factor: A parameter passed directly to the dataloader, with a default value of 10.
diff --git a/docs/source_en/Instruction/Supported-models-and-datasets.md b/docs/source_en/Instruction/Supported-models-and-datasets.md
index adaace369..d5c2e475a 100644
--- a/docs/source_en/Instruction/Supported-models-and-datasets.md
+++ b/docs/source_en/Instruction/Supported-models-and-datasets.md
@@ -173,11 +173,11 @@ The table below introduces the models integrated with ms-swift:
 |[Qwen/Qwen2.5-Math-1.5B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-1.5B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B)|
 |[Qwen/Qwen2.5-Math-7B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-7B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B)|
 |[Qwen/Qwen2.5-Math-72B](https://modelscope.cn/models/Qwen/Qwen2.5-Math-72B)|qwen2_5_math|qwen2_5_math|transformers>=4.37|✔|math|[Qwen/Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B)|
-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat)|
-|[Qwen/Qwen1.5-MoE-A2.7B](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B)|
+|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat)|
+|[Qwen/Qwen1.5-MoE-A2.7B](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B)|
 |[Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4)|
-|[Qwen/Qwen2-57B-A14B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)|
-|[Qwen/Qwen2-57B-A14B](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B](https://huggingface.co/Qwen/Qwen2-57B-A14B)|
+|[Qwen/Qwen2-57B-A14B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)|
+|[Qwen/Qwen2-57B-A14B](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B)|qwen2_moe|qwen|transformers>=4.40|✔|-|[Qwen/Qwen2-57B-A14B](https://huggingface.co/Qwen/Qwen2-57B-A14B)|
 |[Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4)|qwen2_moe|qwen|transformers>=4.40|✘|-|[Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GPTQ-Int4)|
 |[Qwen/QwQ-32B-Preview](https://modelscope.cn/models/Qwen/QwQ-32B-Preview)|qwq_preview|qwq_preview|transformers>=4.37|✔|-|[Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)|
 |[Qwen/QwQ-32B](https://modelscope.cn/models/Qwen/QwQ-32B)|qwq|qwq|transformers>=4.37|✔|-|[Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)|
diff --git a/examples/train/megatron/moe.sh b/examples/train/megatron/moe.sh
new file mode 100644
index 000000000..34e2f8f2c
--- /dev/null
+++ b/examples/train/megatron/moe.sh
@@ -0,0 +1,32 @@
+# 8 * 65GiB
+NPROC_PER_NODE=8 \
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+megatron sft \
+    --load Qwen1.5-MoE-A2.7B-mcore \
+    --dataset 'liucong/Chinese-DeepSeek-R1-Distill-data-110k-SFT' \
+    --tensor_model_parallel_size 2 \
+    --expert_model_parallel_size 4 \
+    --moe_grouped_gemm true \
+    --moe_shared_expert_overlap true \
+    --moe_aux_loss_coeff 0.01 \
+    --micro_batch_size 1 \
+    --global_batch_size 16 \
+    --packing true \
+    --recompute_granularity selective \
+    --train_iters 2000 \
+    --eval_iters 50 \
+    --finetune true \
+    --cross_entropy_loss_fusion true \
+    --lr 1e-5 \
+    --lr_warmup_iters 100 \
+    --min_lr 1e-6 \
+    --save megatron_output/Qwen1.5-MoE-A2.7B \
+    --eval_interval 200 \
+    --save_interval 200 \
+    --max_length 8192 \
+    --num_workers 8 \
+    --dataset_num_proc 8 \
+    --no_save_optim true \
+    --no_save_rng true \
+    --sequence_parallel true \
+    --use_flash_attn true
diff --git a/swift/llm/argument/train_args.py b/swift/llm/argument/train_args.py
index 60b07cd0a..25e18eac6 100644
--- a/swift/llm/argument/train_args.py
+++ b/swift/llm/argument/train_args.py
@@ -136,6 +136,9 @@ def _init_lazy_tokenize(self):
             logger.info(f'Setting args.lazy_tokenize: {self.lazy_tokenize}')
 
     def __post_init__(self) -> None:
+        if self.packing and self.attn_impl != 'flash_attn':
+            logger.warning('The "packing" feature needs to be used in conjunction with "flash_attn". '
+                           'Please specify `--attn_impl flash_attn`.')
         if self.resume_from_checkpoint:
             self.resume_from_checkpoint = to_abspath(self.resume_from_checkpoint, True)
             if self.train_type == 'full':
diff --git a/swift/megatron/argument/megatron_args.py b/swift/megatron/argument/megatron_args.py
index a31df06cd..a1cf7f7f6 100644
--- a/swift/megatron/argument/megatron_args.py
+++ b/swift/megatron/argument/megatron_args.py
@@ -5,6 +5,7 @@
 from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 import torch
+from transformers.utils.versions import require_version
 
 from swift.llm.argument.base_args import to_abspath
 
@@ -15,6 +16,8 @@ class ExtraMegatronArguments:
     rope_scaling: Optional[Union[dict, str]] = None
     torch_dtype: Optional[torch.dtype] = None
     model_type: Optional[str] = None
+    dataloader_persistent_workers: bool = True
+    dataloader_prefetch_factor: int = 10
 
 
 @dataclass
@@ -90,23 +93,39 @@ class MegatronArguments(ExtraMegatronArguments):
     ffn_hidden_size: Optional[int] = None
     num_attention_heads: Optional[int] = None
     group_query_attention: Optional[bool] = None
-    num_query_groups: int = 1
+    num_query_groups: Optional[int] = None
     max_position_embeddings: Optional[int] = None
     position_embedding_type: Literal['learned_absolute', 'rope', 'relative', 'none'] = 'rope'
-    rotary_base: int = 10000
+    rotary_base: Optional[int] = None
     rotary_percent: float = 1.
     normalization: Literal['LayerNorm', 'RMSNorm'] = 'RMSNorm'
-    norm_epsilon: float = 1e-5
-    swiglu: bool = True
-    untie_embeddings_and_output_weights: bool = True
-    disable_bias_linear: bool = True
-    add_qkv_bias: bool = True
-    attention_dropout: float = 0.
+    norm_epsilon: Optional[float] = None
+    swiglu: Optional[bool] = None
+    untie_embeddings_and_output_weights: Optional[bool] = None
+    disable_bias_linear: Optional[bool] = None
+    add_qkv_bias: Optional[bool] = None
+    attention_dropout: Optional[float] = None
     hidden_dropout: float = 0.
     kv_channels: Optional[int] = None
-    qk_layernorm: bool = False
+    qk_layernorm: Optional[bool] = None
     transformer_impl: Literal['local', 'transformer_engine'] = 'transformer_engine'
 
+    # moe
+    num_experts: Optional[int] = None
+    moe_ffn_hidden_size: Optional[int] = None
+    moe_shared_expert_intermediate_size: Optional[int] = None
+    moe_router_topk: Optional[int] = None
+    moe_router_pre_softmax: Optional[bool] = None
+    moe_aux_loss_coeff: Optional[float] = None
+
+    expert_model_parallel_size: int = 1
+    moe_token_dispatcher_type: Literal['allgather', 'alltoall', 'alltoall_seq'] = 'alltoall'
+    moe_grouped_gemm: bool = False
+    moe_router_load_balancing_type: Literal['aux_loss', 'seq_aux_loss', 'sinkhorn', 'none'] = 'aux_loss'
+    moe_z_loss_coeff: Optional[float] = None
+    moe_expert_capacity_factor: Optional[float] = None
+    moe_shared_expert_overlap: bool = False
+
     # mixed precision
     fp16: Optional[bool] = None
     bf16: Optional[bool] = None
@@ -114,7 +133,7 @@ class MegatronArguments(ExtraMegatronArguments):
     attention_softmax_in_fp32: bool = True
 
     # logging
-    log_params_norm: bool = True
+    log_params_norm: bool = False
     log_throughput: bool = True
     tensorboard_log_interval: int = 1
     tensorboard_queue_size: int = 50
@@ -134,6 +153,32 @@ class MegatronArguments(ExtraMegatronArguments):
     num_workers: int = 4
     no_create_attention_mask_in_dataloader: bool = True
 
+    def _set_default(self):
+        if self.num_query_groups is None:
+            self.num_query_groups = 1
+        if self.norm_epsilon is None:
+            self.norm_epsilon = 1e-5
+        if self.rotary_base is None:
+            self.rotary_base = 10000
+        if self.attention_dropout is None:
+            self.attention_dropout = 0.
+        if self.untie_embeddings_and_output_weights is None:
+            self.untie_embeddings_and_output_weights = True
+        if self.swiglu is None:
+            self.swiglu = True
+        if self.add_qkv_bias is None:
+            self.add_qkv_bias = True
+        if self.disable_bias_linear is None:
+            self.disable_bias_linear = True
+        if self.moe_router_topk is None:
+            self.moe_router_topk = 2
+        if self.moe_router_pre_softmax is None:
+            self.moe_router_pre_softmax = False
+        if self.moe_aux_loss_coeff is None:
+            self.moe_aux_loss_coeff = 0.
+        if self.qk_layernorm is None:
+            self.qk_layernorm = False
+
     def _init_mixed_precision(self):
         from swift.llm.argument.base_args.model_args import ModelArguments
         ModelArguments._init_mixed_precision(self)
@@ -142,9 +187,20 @@ def _init_mixed_precision(self):
         if self.apply_query_key_layer_scaling:
             os.environ['NVTE_APPLY_QK_LAYER_SCALING'] = '1'
 
+    def _init_moe(self):
+        if self.moe_shared_expert_intermediate_size == 0:
+            self.moe_shared_expert_intermediate_size = None
+        if self.moe_ffn_hidden_size is None:
+            self.moe_ffn_hidden_size = self.ffn_hidden_size
+        else:
+            self.ffn_hidden_size = self.moe_ffn_hidden_size
+
     def __post_init__(self):
         from swift.llm.argument.base_args.model_args import ModelArguments
+        if self.use_flash_attn:
+            require_version('flash-attn')
         os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1'
+        self._set_default()
         self.group_query_attention = self.num_query_groups > 1
         if self.rope_scaling is not None:
             self.rope_scaling = ModelArguments.parse_to_dict(self.rope_scaling)
@@ -154,6 +210,7 @@ def __post_init__(self):
             self.seq_length = self.max_position_embeddings
         if self.tensorboard_dir is None and self.save is not None:
             self.tensorboard_dir = f'{self.save}/runs'
+        self._init_moe()
         self._init_mixed_precision()
 
         self.tensorboard_dir = to_abspath(self.tensorboard_dir)
diff --git a/swift/megatron/argument/train_args.py b/swift/megatron/argument/train_args.py
index 3c89b2eb4..7627515c4 100644
--- a/swift/megatron/argument/train_args.py
+++ b/swift/megatron/argument/train_args.py
@@ -24,7 +24,8 @@ def init_model_args(self, config):
         self.megatron_model_meta = get_megatron_model_meta(self.model_type)
         kwargs = self.megatron_model_meta.convert_hf_config(config)
         for k, v in kwargs.items():
-            setattr(self, k, v)
+            if getattr(self, k) is None:
+                setattr(self, k, v)
         MegatronArguments.__post_init__(self)
         self.extra_args = self.parse_to_megatron()
 
diff --git a/swift/megatron/init.py b/swift/megatron/init.py
index 4eca7959d..90016688c 100644
--- a/swift/megatron/init.py
+++ b/swift/megatron/init.py
@@ -6,6 +6,18 @@
 from swift.utils import is_megatron_available, safe_ddp_context, subprocess_run
 
 
+def _patch_megatron():
+    try:
+        from transformer_engine.pytorch.attention import FusedRoPEFunc
+    except ImportError:
+        try:
+            import transformer_engine
+            transformer_engine.pytorch.attention.FusedRoPEFunc = (
+                transformer_engine.pytorch.dot_product_attention.rope.FusedRoPEFunc)
+        except (ImportError, AttributeError):
+            pass
+
+
 def init_megatron_env() -> None:
     if 'MEGATRON_LM_PATH' not in os.environ:
         os.environ['MEGATRON_LM_PATH'] = git_clone_github(
@@ -14,3 +26,4 @@ def init_megatron_env() -> None:
         if not is_megatron_available():
             subprocess_run([sys.executable, '-m', 'pip', 'install', '-e', os.environ['MEGATRON_LM_PATH']])
     sys.path.insert(0, os.environ['MEGATRON_LM_PATH'])
+    _patch_megatron()
diff --git a/swift/megatron/model/config.py b/swift/megatron/model/config.py
index f7a640bee..bd9c9656c 100644
--- a/swift/megatron/model/config.py
+++ b/swift/megatron/model/config.py
@@ -21,6 +21,13 @@
     'disable_bias_linear': ['mlp_bias'],
     'kv_channels': ['head_dim'],
     'model_type': ['model_type'],
+    # moe
+    'moe_ffn_hidden_size': ['moe_intermediate_size'],
+    'moe_shared_expert_intermediate_size': ['shared_expert_intermediate_size'],
+    'moe_router_topk': ['num_experts_per_tok'],
+    'num_experts': ['num_experts'],
+    'moe_router_pre_softmax': ['norm_topk_prob'],
+    'moe_aux_loss_coeff': ['router_aux_loss_coef'],
 }
 
 
@@ -32,7 +39,7 @@ def convert_hf_config(config) -> Dict[str, Any]:
                 hf_v = getattr(config, hf_k)
                 if k == 'rotary_base':
                     megatron_config[k] = int(hf_v)
-                elif k in {'untie_embeddings_and_output_weights', 'disable_bias_linear'}:
+                elif k in {'untie_embeddings_and_output_weights', 'disable_bias_linear', 'moe_router_pre_softmax'}:
                     megatron_config[k] = not hf_v
                 elif k == 'swiglu':
                     if hf_v == 'silu':
diff --git a/swift/megatron/model/gpt/__init__.py b/swift/megatron/model/gpt/__init__.py
index ab3756aa5..8d9af9f71 100644
--- a/swift/megatron/model/gpt/__init__.py
+++ b/swift/megatron/model/gpt/__init__.py
@@ -35,4 +35,6 @@
         ModelType.ziya,
         ModelType.mengzi3,
         ModelType.qwen3,
+        ModelType.qwen2_moe,
+        ModelType.qwen3_moe,
     ], model_provider, convert_gpt_hf_config, convert_mcore2hf, convert_hf2mcore))
diff --git a/swift/megatron/model/gpt/config.py b/swift/megatron/model/gpt/config.py
index 5adb46b63..6658a952a 100644
--- a/swift/megatron/model/gpt/config.py
+++ b/swift/megatron/model/gpt/config.py
@@ -5,6 +5,9 @@
 
 def convert_gpt_hf_config(config) -> Dict[str, Any]:
     res = convert_hf_config(config)
-    if res.get('model_type') == 'qwen3':
+    model_type = res.get('model_type')
+    if model_type in {'qwen3', 'qwen3_moe'}:
         res['qk_layernorm'] = True
+    if model_type in {'qwen2_moe', 'qwen3_moe'}:
+        res.pop('ffn_hidden_size', None)
     return res
diff --git a/swift/megatron/model/gpt/hf2mcore.py b/swift/megatron/model/gpt/hf2mcore.py
index 8725dd4b2..46525df3c 100644
--- a/swift/megatron/model/gpt/hf2mcore.py
+++ b/swift/megatron/model/gpt/hf2mcore.py
@@ -3,10 +3,7 @@
 from megatron.training import get_args
 
 
-def set_attn_state(args, mg_layer, hf_layer):
-    mg_attn = mg_layer.self_attention
-    hf_attn = hf_layer.self_attn
-
+def set_attn_state(args, mg_attn, hf_attn):
     num_query_groups = (args.num_query_groups if args.group_query_attention else args.num_attention_heads)
 
     # Copy weights
@@ -33,19 +30,37 @@ def set_attn_state(args, mg_layer, hf_layer):
         mg_attn.k_layernorm.weight.data.copy_(hf_attn.k_norm.weight)
 
 
-def set_mlp_state(args, mg_layer, hf_layer):
-    mg_layer.mlp.linear_fc1.weight.data.copy_(
-        torch.cat([hf_layer.mlp.gate_proj.weight, hf_layer.mlp.up_proj.weight], dim=0))
-    mg_layer.mlp.linear_fc2.weight.data.copy_(hf_layer.mlp.down_proj.weight)
+def _set_mlp_state(mg_mlp, hf_mlp):
+    mg_mlp.linear_fc1.weight.data.copy_(torch.cat([hf_mlp.gate_proj.weight, hf_mlp.up_proj.weight], dim=0))
+    mg_mlp.linear_fc2.weight.data.copy_(hf_mlp.down_proj.weight)
+
+
+def set_mlp_state(args, mg_mlp, hf_mlp):
+    if args.num_experts:
+        mg_mlp.router.weight.data.copy_(hf_mlp.gate.weight)
+        if mg_mlp.shared_experts is not None:
+            mg_mlp.shared_experts.gate_weight.data.copy_(hf_mlp.shared_expert_gate.weight)
+        for expert_idx in range(args.num_experts):
+            _set_mlp_state(mg_mlp.experts.local_experts[expert_idx], hf_mlp.experts[expert_idx])
+
+        if mg_mlp.shared_experts is not None:
+            _set_mlp_state(mg_mlp.shared_experts, hf_mlp.shared_expert)
+    else:
+        _set_mlp_state(mg_mlp, hf_mlp)
 
 
 def set_layer_state(args, mg_model, hf_model, layer_idx):
     mg_layer = mg_model.decoder.layers[layer_idx]
     hf_layer = hf_model.model.layers[layer_idx]
-    # self-attention
-    set_attn_state(args, mg_layer, hf_layer)
-    set_mlp_state(args, mg_layer, hf_layer)
-    mg_layer.mlp.linear_fc1.layer_norm_weight.data.copy_(hf_layer.post_attention_layernorm.weight)
+
+    set_attn_state(args, mg_layer.self_attention, hf_layer.self_attn)
+    set_mlp_state(args, mg_layer.mlp, hf_layer.mlp)
+
+    post_attention_layernorm_weight = hf_layer.post_attention_layernorm.weight
+    if args.num_experts:
+        mg_layer.pre_mlp_layernorm.weight.data.copy_(post_attention_layernorm_weight)
+    else:
+        mg_layer.mlp.linear_fc1.layer_norm_weight.data.copy_(post_attention_layernorm_weight)
     mg_layer.self_attention.linear_qkv.layer_norm_weight.data.copy_(hf_layer.input_layernorm.weight)
 
 
diff --git a/swift/megatron/model/gpt/mcore2hf.py b/swift/megatron/model/gpt/mcore2hf.py
index 4ada232e5..6f29abaf0 100644
--- a/swift/megatron/model/gpt/mcore2hf.py
+++ b/swift/megatron/model/gpt/mcore2hf.py
@@ -2,10 +2,7 @@
 from megatron.training import get_args
 
 
-def set_attn_state(args, mg_layer, hf_layer):
-    mg_attn = mg_layer.self_attention
-    hf_attn = hf_layer.self_attn
-
+def set_attn_state(args, mg_attn, hf_attn):
     num_query_groups = (args.num_query_groups if args.group_query_attention else args.num_attention_heads)
     # Copy weights
     mg_attn_weight = mg_attn.linear_qkv.weight.reshape((num_query_groups, -1, args.hidden_size))
@@ -28,18 +25,38 @@ def set_attn_state(args, mg_layer, hf_layer):
         hf_attn.k_norm.weight.data.copy_(mg_attn.k_layernorm.weight)
 
 
-def set_mlp_state(args, mg_layer, hf_layer):
-    hf_layer.mlp.gate_proj.weight.data.copy_(mg_layer.mlp.linear_fc1.weight[:args.ffn_hidden_size])
-    hf_layer.mlp.up_proj.weight.data.copy_(mg_layer.mlp.linear_fc1.weight[args.ffn_hidden_size:])
-    hf_layer.mlp.down_proj.weight.data.copy_(mg_layer.mlp.linear_fc2.weight)
+def _set_mlp_state(mg_mlp, hf_mlp):
+    ffn_hidden_size = hf_mlp.gate_proj.weight.shape[0]
+    hf_mlp.gate_proj.weight.data.copy_(mg_mlp.linear_fc1.weight[:ffn_hidden_size])
+    hf_mlp.up_proj.weight.data.copy_(mg_mlp.linear_fc1.weight[ffn_hidden_size:])
+    hf_mlp.down_proj.weight.data.copy_(mg_mlp.linear_fc2.weight)
+
+
+def set_mlp_state(args, mg_mlp, hf_mlp):
+    if args.num_experts:
+        hf_mlp.gate.weight.data.copy_(mg_mlp.router.weight)
+        if mg_mlp.shared_experts is not None:
+            hf_mlp.shared_expert_gate.weight.data.copy_(mg_mlp.shared_experts.gate_weight)
+        for expert_idx in range(args.num_experts):
+            _set_mlp_state(mg_mlp.experts.local_experts[expert_idx], hf_mlp.experts[expert_idx])
+
+        if mg_mlp.shared_experts is not None:
+            _set_mlp_state(mg_mlp.shared_experts, hf_mlp.shared_expert)
+    else:
+        _set_mlp_state(mg_mlp, hf_mlp)
 
 
 def set_layer_state(args, mg_model, hf_model, layer_idx):
     mg_layer = mg_model.decoder.layers[layer_idx]
     hf_layer = hf_model.model.layers[layer_idx]
-    set_attn_state(args, mg_layer, hf_layer)
-    set_mlp_state(args, mg_layer, hf_layer)
-    hf_layer.post_attention_layernorm.weight.data.copy_(mg_layer.mlp.linear_fc1.layer_norm_weight)
+    set_attn_state(args, mg_layer.self_attention, hf_layer.self_attn)
+    set_mlp_state(args, mg_layer.mlp, hf_layer.mlp)
+
+    post_attention_layernorm_weight = hf_layer.post_attention_layernorm.weight
+    if args.num_experts:
+        post_attention_layernorm_weight.data.copy_(mg_layer.pre_mlp_layernorm.weight)
+    else:
+        post_attention_layernorm_weight.data.copy_(mg_layer.mlp.linear_fc1.layer_norm_weight)
     hf_layer.input_layernorm.weight.data.copy_(mg_layer.self_attention.linear_qkv.layer_norm_weight)
 
 
diff --git a/swift/megatron/model/gpt/model.py b/swift/megatron/model/gpt/model.py
index e23515b6f..9bc6bf4fb 100644
--- a/swift/megatron/model/gpt/model.py
+++ b/swift/megatron/model/gpt/model.py
@@ -13,6 +13,9 @@ def model_provider(pre_process=True, post_process=True):
     config.variable_seq_lengths = True
     transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec(args.num_experts, args.moe_grouped_gemm,
                                                                         args.qk_layernorm, args.multi_latent_attention)
+    if args.num_experts and args.moe_shared_expert_intermediate_size:
+        # qwen2_moe/qwen3_moe
+        transformer_layer_spec.submodules.mlp.submodules.shared_experts.params = {'gate': True}
     model = GPTModel(
         config=config,
         transformer_layer_spec=transformer_layer_spec,
diff --git a/swift/megatron/train/patcher.py b/swift/megatron/train/patcher.py
index d1f09b98a..76a986242 100644
--- a/swift/megatron/train/patcher.py
+++ b/swift/megatron/train/patcher.py
@@ -26,9 +26,10 @@ def training_log(loss_dict, total_loss_dict, learning_rate, decoupled_learning_r
                 if isinstance(v, torch.Tensor):
                     v = v.item()
                 logs[k] = round(v, 8)
-            logs['grad_norm'] = round(grad_norm, 8)
-            logs['params_norm'] = round(params_norm, 8)
-            logs['learning_rate'] = round(learning_rate, 8)
+            for k in {'grad_norm', 'params_norm', 'learning_rate'}:
+                v = locals()[k]
+                if v is not None:
+                    logs[k] = round(v, 8)
             logs['consumed_samples'] = args.consumed_train_samples
             logs['global_step/max_steps'] = f'{iteration}/{args.train_iters}'
             if jsonl_writer is None:
diff --git a/swift/megatron/train/utils.py b/swift/megatron/train/utils.py
index 06fe8d34f..21833014d 100644
--- a/swift/megatron/train/utils.py
+++ b/swift/megatron/train/utils.py
@@ -40,7 +40,8 @@ def build_streaming_dataloader(args, dataset, collate_fn):
         pin_memory=True,
         collate_fn=collate_fn,
         batch_size=args.micro_batch_size,
-        prefetch_factor=10,
+        prefetch_factor=args.dataloader_prefetch_factor,
+        persistent_workers=args.dataloader_persistent_workers,
     )
     return iter(cyclic_iter(MegatronDataLoaderDispatcher(base_dataloader)))
 
diff --git a/tests/megatron/test_align/test_llm.py b/tests/megatron/test_align/test_llm.py
index e1c981453..7c5cdf394 100644
--- a/tests/megatron/test_align/test_llm.py
+++ b/tests/megatron/test_align/test_llm.py
@@ -1,11 +1,33 @@
 import os
 
+import torch
+
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
 
 def _test_model(model_id):
     from swift.llm import export_main, ExportArguments
-    export_main(ExportArguments(model=model_id, to_mcore=True, exist_ok=True, test_convert_precision=True))
+    if model_id.endswith('mcore'):
+        export_main(
+            ExportArguments(
+                mcore_model=model_id,
+                to_hf=True,
+                exist_ok=True,
+                test_convert_precision=True,
+                torch_dtype=torch.bfloat16))
+    else:
+        export_main(
+            ExportArguments(
+                model=model_id,
+                to_mcore=True,
+                exist_ok=True,
+                test_convert_precision=True,
+                torch_dtype=torch.bfloat16,
+            ))
+
+
+def test_qwen2():
+    _test_model('Qwen/Qwen2-0.5B-Instruct')
 
 
 def test_llama2():
@@ -21,7 +43,6 @@ def test_marco_o1():
 
 
 def test_deepseek_r1_llama():
-    # TODO: FIX rope
     _test_model('deepseek-ai/DeepSeek-R1-Distill-Llama-8B')
 
 
@@ -49,7 +70,16 @@ def test_qwen3():
     _test_model('Qwen/Qwen3-0.6B-Base')
 
 
+def test_qwen2_moe():
+    _test_model('Qwen/Qwen1.5-MoE-A2.7B-Chat')
+
+
+def test_qwen3_moe():
+    _test_model('Qwen/Qwen3-15B-A2B-Base')
+
+
 if __name__ == '__main__':
+    # test_qwen2()
     # test_llama2()
     # test_llama3()
     # test_marco_o1()
@@ -59,4 +89,6 @@ def test_qwen3():
     # test_megrez()
     # test_llama3_1()
     # test_llama3_2()
-    test_qwen3()
+    # test_qwen3()
+    # test_qwen2_moe()
+    test_qwen3_moe()