[megatron] support multimodal model CPT/SFT/DPO (Full/LoRA) (#5502)

Author: Jintao-Huang

README.md

@@ -75,6 +75,7 @@ You can contact us and communicate with us by adding our group:
 
 
 ## 🎉 News
+- 🎁 2025.09.02: Megatron-SWIFT now supports multimodal model training. Documentation can be found [here](./docs/source_en/Megatron-SWIFT/Multimodal-Model.md).
 - 🎁 2025.08.12: Support [Dynamic Fine-Tuning](https://arxiv.org/abs/2508.05629)(DFT) in SFT training, use parameter `--enable_dft_loss true`. Training scripts can be found [here](https://github.com/modelscope/ms-swift/blob/main/examples/train/full/dft.sh).
 - 🎁 2025.07.12: Deployment(pt/vLLM/SGLang) of Embedding models is supported, check [here](examples/deploy/embedding/client.py).
 - 🎁 2025.07.09: Megatron-SWIFT supports LoRA training. Compared to ms-swift, it achieves significant speedup on MoE models. Training scripts can be found [here](https://github.com/modelscope/ms-swift/blob/main/examples/megatron/lora).

README_CN.md

@@ -71,6 +71,7 @@
 - **模型量化**：支持AWQ、GPTQ、FP8和BNB的量化导出，导出的模型支持使用vLLM/SGLang/LmDeploy推理加速，并支持继续训练。
 
 ## 🎉 新闻
+- 🎁 2025.09.02: Megatron-SWIFT支持多模态模型训练。文档参考[这里](./docs/source/Megatron-SWIFT/多模态模型.md)。
 - 🎁 2025.08.12: 支持在SFT训练中使用[Dynamic Fine-Tuning](https://arxiv.org/abs/2508.05629)(DFT)，使用参数 `--enable_dft_loss true`。训练脚本参考[这里](https://github.com/modelscope/ms-swift/blob/main/examples/train/full/dft.sh)
 - 🎁 2025.07.12: 支持部署Embedding模型的部署(pt/vLLM/SGLang), 查看[这里](examples/deploy/embedding/client.py).
 - 🎁 2025.07.09: Megatron-SWIFT支持LoRA训练。相比ms-swift，在MoE模型提速显著。训练脚本参考[这里](https://github.com/modelscope/ms-swift/blob/main/examples/megatron/lora)。

docs/source/Instruction/命令行参数.md

@@ -159,7 +159,7 @@
 - 🔥aligner_lr: 当训练多模态大模型时，该参数指定aligner的学习率，默认为None，等于learning_rate。
 - lr_scheduler_type: lr_scheduler类型，默认为'cosine'。
 - lr_scheduler_kwargs: lr_scheduler其他参数。默认为None。
-- 🔥gradient_checkpointing_kwargs: 传入`torch.utils.checkpoint`中的参数。例如设置为`--gradient_checkpointing_kwargs '{"use_reentrant": false}'`。默认为None。
+- gradient_checkpointing_kwargs: 传入`torch.utils.checkpoint`中的参数。例如设置为`--gradient_checkpointing_kwargs '{"use_reentrant": false}'`。默认为None。
   - 注意：当使用DDP而不使用deepspeed/fsdp，且gradient_checkpointing_kwargs为None，会默认设置其为`'{"use_reentrant": false}'`。
 - full_determinism: 确保训练中获得可重现的结果，注意：这会对性能产生负面影响。默认为False。
 - 🔥report_to: 默认值为`tensorboard`。你也可以指定`--report_to tensorboard wandb swanlab`、`--report_to all`。
@@ -211,10 +211,10 @@
 - hub_private_repo: 默认为False。
 
 ### Tuner参数
-- 🔥freeze_llm: 该参数只对多模态模型生效，可用于全参和LoRA，但含义不同。若是全参数训练，将freeze_llm设置为True将会将llm部分权重进行冻结，若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_llm设置为True将会取消在llm部分添加LoRA模块。该参数默认为False。
-- 🔥freeze_vit: 该参数只对多模态模型生效，可用于全参和LoRA，含义参考`freeze_llm`。默认为True。
+- 🔥freeze_llm: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_llm设置为True将会将LLM部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_llm设置为True将会取消在LLM部分添加LoRA模块。该参数默认为False。
+- 🔥freeze_vit: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_vit设置为True将会将vit部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_vit设置为True将会取消在vit部分添加LoRA模块。该参数默认为True。
   - 注意：这里的vit不仅限于vision_tower, 也包括audio_tower。
-- 🔥freeze_aligner: 该参数只对多模态模型生效，可用于全参和LoRA，含义参考`freeze_llm`。默认为True。
+- 🔥freeze_aligner: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_aligner设置为True将会将aligner（也称为projector）部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_aligner设置为True将会取消在aligner部分添加LoRA模块。该参数默认为True。
 - 🔥target_modules: 指定lora模块, 默认为`['all-linear']`。你也可以设置为module的后缀，例如：`--target_modules q_proj k_proj v_proj`。该参数不限于LoRA，可用于其他tuners。
   - 注意：在LLM和多模态LLM中，'all-linear'的行为有所不同。若是LLM则自动寻找除lm_head外的linear并附加tuner；若是多模态LLM，则默认只在LLM上附加tuner，该行为可以被`freeze_llm`、`freeze_vit`、`freeze_aligner`控制。
 - 🔥target_regex: 指定lora模块的regex表达式，默认为`None`。如果该值传入，则target_modules参数失效。该参数不限于LoRA，可用于其他tuners。

docs/source/Instruction/支持的模型和数据集.md

@@ -652,12 +652,12 @@
 |[Qwen/Qwen-VL-Chat-Int4](https://modelscope.cn/models/Qwen/Qwen-VL-Chat-Int4)|qwen_vl|qwen_vl|-|✘|vision|[Qwen/Qwen-VL-Chat-Int4](https://huggingface.co/Qwen/Qwen-VL-Chat-Int4)|
 |[Qwen/Qwen-Audio-Chat](https://modelscope.cn/models/Qwen/Qwen-Audio-Chat)|qwen_audio|qwen_audio|-|✘|audio|[Qwen/Qwen-Audio-Chat](https://huggingface.co/Qwen/Qwen-Audio-Chat)|
 |[Qwen/Qwen-Audio](https://modelscope.cn/models/Qwen/Qwen-Audio)|qwen_audio|qwen_audio|-|✘|audio|[Qwen/Qwen-Audio](https://huggingface.co/Qwen/Qwen-Audio)|
-|[Qwen/Qwen2-VL-2B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)|
-|[Qwen/Qwen2-VL-7B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)|
-|[Qwen/Qwen2-VL-72B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-72B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct)|
-|[Qwen/Qwen2-VL-2B](https://modelscope.cn/models/Qwen/Qwen2-VL-2B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B)|
-|[Qwen/Qwen2-VL-7B](https://modelscope.cn/models/Qwen/Qwen2-VL-7B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-7B](https://huggingface.co/Qwen/Qwen2-VL-7B)|
-|[Qwen/Qwen2-VL-72B](https://modelscope.cn/models/Qwen/Qwen2-VL-72B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-72B](https://huggingface.co/Qwen/Qwen2-VL-72B)|
+|[Qwen/Qwen2-VL-2B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)|
+|[Qwen/Qwen2-VL-7B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)|
+|[Qwen/Qwen2-VL-72B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-VL-72B-Instruct)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct)|
+|[Qwen/Qwen2-VL-2B](https://modelscope.cn/models/Qwen/Qwen2-VL-2B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B)|
+|[Qwen/Qwen2-VL-7B](https://modelscope.cn/models/Qwen/Qwen2-VL-7B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-7B](https://huggingface.co/Qwen/Qwen2-VL-7B)|
+|[Qwen/Qwen2-VL-72B](https://modelscope.cn/models/Qwen/Qwen2-VL-72B)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2-VL-72B](https://huggingface.co/Qwen/Qwen2-VL-72B)|
 |[Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4)|
 |[Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4)|
 |[Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4](https://modelscope.cn/models/Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4)|
@@ -667,16 +667,16 @@
 |[Qwen/Qwen2-VL-2B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct-AWQ)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-2B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-AWQ)|
 |[Qwen/Qwen2-VL-7B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct-AWQ)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-7B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct-AWQ)|
 |[Qwen/Qwen2-VL-72B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2-VL-72B-Instruct-AWQ)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2-VL-72B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct-AWQ)|
-|[bytedance-research/UI-TARS-2B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-2B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[bytedance-research/UI-TARS-2B-SFT](https://huggingface.co/bytedance-research/UI-TARS-2B-SFT)|
-|[bytedance-research/UI-TARS-7B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-7B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[bytedance-research/UI-TARS-7B-SFT](https://huggingface.co/bytedance-research/UI-TARS-7B-SFT)|
-|[bytedance-research/UI-TARS-7B-DPO](https://modelscope.cn/models/bytedance-research/UI-TARS-7B-DPO)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[bytedance-research/UI-TARS-7B-DPO](https://huggingface.co/bytedance-research/UI-TARS-7B-DPO)|
-|[bytedance-research/UI-TARS-72B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-72B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[bytedance-research/UI-TARS-72B-SFT](https://huggingface.co/bytedance-research/UI-TARS-72B-SFT)|
-|[bytedance-research/UI-TARS-72B-DPO](https://modelscope.cn/models/bytedance-research/UI-TARS-72B-DPO)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[bytedance-research/UI-TARS-72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO)|
-|[allenai/olmOCR-7B-0225-preview](https://modelscope.cn/models/allenai/olmOCR-7B-0225-preview)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[allenai/olmOCR-7B-0225-preview](https://huggingface.co/allenai/olmOCR-7B-0225-preview)|
-|[Qwen/Qwen2.5-VL-3B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-3B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)|
-|[Qwen/Qwen2.5-VL-7B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)|
-|[Qwen/Qwen2.5-VL-32B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-32B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct)|
-|[Qwen/Qwen2.5-VL-72B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-72B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct)|
+|[bytedance-research/UI-TARS-2B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-2B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[bytedance-research/UI-TARS-2B-SFT](https://huggingface.co/bytedance-research/UI-TARS-2B-SFT)|
+|[bytedance-research/UI-TARS-7B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-7B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[bytedance-research/UI-TARS-7B-SFT](https://huggingface.co/bytedance-research/UI-TARS-7B-SFT)|
+|[bytedance-research/UI-TARS-7B-DPO](https://modelscope.cn/models/bytedance-research/UI-TARS-7B-DPO)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[bytedance-research/UI-TARS-7B-DPO](https://huggingface.co/bytedance-research/UI-TARS-7B-DPO)|
+|[bytedance-research/UI-TARS-72B-SFT](https://modelscope.cn/models/bytedance-research/UI-TARS-72B-SFT)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[bytedance-research/UI-TARS-72B-SFT](https://huggingface.co/bytedance-research/UI-TARS-72B-SFT)|
+|[bytedance-research/UI-TARS-72B-DPO](https://modelscope.cn/models/bytedance-research/UI-TARS-72B-DPO)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[bytedance-research/UI-TARS-72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO)|
+|[allenai/olmOCR-7B-0225-preview](https://modelscope.cn/models/allenai/olmOCR-7B-0225-preview)|qwen2_vl|qwen2_vl|transformers>=4.45, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[allenai/olmOCR-7B-0225-preview](https://huggingface.co/allenai/olmOCR-7B-0225-preview)|
+|[Qwen/Qwen2.5-VL-3B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-3B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)|
+|[Qwen/Qwen2.5-VL-7B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)|
+|[Qwen/Qwen2.5-VL-32B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-32B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct)|
+|[Qwen/Qwen2.5-VL-72B-Instruct](https://modelscope.cn/models/Qwen/Qwen2.5-VL-72B-Instruct)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✔|vision, video|[Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct)|
 |[Qwen/Qwen2.5-VL-3B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2.5-VL-3B-Instruct-AWQ)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-3B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ)|
 |[Qwen/Qwen2.5-VL-7B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct-AWQ)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-7B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct-AWQ)|
 |[Qwen/Qwen2.5-VL-32B-Instruct-AWQ](https://modelscope.cn/models/Qwen/Qwen2.5-VL-32B-Instruct-AWQ)|qwen2_5_vl|qwen2_5_vl|transformers>=4.49, qwen_vl_utils>=0.0.6, decord|✘|vision, video|[Qwen/Qwen2.5-VL-32B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct-AWQ)|

docs/source/Megatron-SWIFT/命令行参数.md

@@ -192,6 +192,10 @@
 
 **Tuner参数**:
 - train_type: 可选为'lora'和'full'。默认为'full'。
+- 🔥freeze_llm: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_llm设置为True将会将LLM部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_llm设置为True将会取消在LLM部分添加LoRA模块。该参数默认为False。
+- 🔥freeze_vit: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_vit设置为True将会将vit部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_vit设置为True将会取消在vit部分添加LoRA模块。该参数默认为True。
+  - 注意：这里的vit不仅限于vision_tower, 也包括audio_tower。
+- 🔥freeze_aligner: 该参数只对多模态模型生效，可用于全参和LoRA，但会产生不同的效果。若是全参数训练，将freeze_aligner设置为True将会将aligner（也称为projector）部分权重进行冻结；若是LoRA训练且`target_modules`设置为'all-linear'，将freeze_aligner设置为True将会取消在aligner部分添加LoRA模块。该参数默认为True。
 
 全参数训练：
 - freeze_parameters: 需要被冻结参数的前缀，默认为`[]`。
@@ -234,6 +238,8 @@ Megatron训练参数继承自Megatron参数和基本参数（与ms-swift共用da
   - 若要自定义attention_mask，你可以设置`--padding_free false`。
   - 注意：Megatron-SWIFT训练特性优先支持padding_free格式，若非特殊情况，请勿修改该值。
 - mlp_padding_free: 默认为False。用于padding_free设置为false时，对mlp进行padding_free优化。这可以在自定义attention_mask的同时，提升训练速度和减少显存占用。
+- vit_gradient_checkpointing: 多模态模型训练时，是否对vit部分开启gradient_checkpointing。默认为True。
+- gradient_checkpointing_kwargs: 传入`torch.utils.checkpoint`中的参数。例如设置为`--gradient_checkpointing_kwargs '{"use_reentrant": false}'`。默认为None。
 - 🔥packing: 是否使用序列packing，默认为False。当前支持CPT/SFT/DPO。
 - packing_length: packing的长度。默认为None，设置为max_length。
 - streaming: 流式读取并处理数据集，默认False。