update

Author: hengtaoguo

docs/tutorials/posttraining/multimodal.md

@@ -170,7 +170,7 @@ Input `<|im_start|>user
 
 Supervised Fine-Tuning (SFT) of multimodal LLMs in MaxText focuses specifically on post-training; we don't yet support pre-training multimodal models from scratch. The SFT process typically involves training on Visual Question Answering (VQA) datasets where the model learns to generate accurate text responses based on both visual and textual inputs. During this fine-tuning phase, we recommend to freeze the pre-trained encoder layers (such as vision transformers) to preserve their learned visual representations, while the projection layers and LLM decoder components remain trainable. This selective training strategy allows the model to adapt the cross-modal alignment and text generation capabilities without disrupting the robust feature extraction abilities of the encoders, ultimately leading to improved performance on multimodal understanding and reasoning tasks while maintaining computational efficiency. This is achieved by setting `freeze_vision_encoder_params=True` in [sft-vision-chartqa.yml](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/configs/post_train/sft-vision-chartqa.yml).
 
-Text+image SFT is supported for all models listed above. The following example uses Gemma3-4B with the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset:
+**Text+image SFT is supported for all models listed above.** The following example uses Gemma3-4B with the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset:
 
 ```shell
 export MAXTEXT_CKPT_PATH=<your-checkpoints-path>  # either set to an already available MaxText ckpt or to the one we just converted in the previous step