fix emb docs (#4434)

Author: tastelikefeet

docs/source/BestPractices/Embedding训练.md

@@ -95,3 +95,17 @@ SWIFT提供了两个脚手架训练脚本：
 
 - [gte模型](https://github.com/tastelikefeet/swift/blob/main/examples/train/embedding/train_gte.sh)
 - [gme模型](https://github.com/tastelikefeet/swift/blob/main/examples/train/embedding/train_gme.sh)
+
+## 推理
+
+SWIFT当前没有支持Embedding的模型推理和部署（时间问题），可以使用原模型的代码进行推理：
+
+https://www.modelscope.cn/models/iic/gte_Qwen2-7B-instruct
+
+https://www.modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct
+
+如果使用了其他模型从0训练embedding（例如，原版`qwen2-vl`模型+`--task_type embedding`），也可以使用gme的推理代码，但请注意：
+
+https://www.modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct/file/view/master/gme_inference.py?status=1#L111
+
+这里的模板请修改为模型自身的template，以免最后的embedding对不上。需要额外注意的是，gme模型的template和`qwen2-vl`或`qwen2.5-vl`系列的chatml template并不相同，其推理代码最后的结束字符是`<|endoftext|>`而非`<|im_end|>`.

docs/source_en/BestPractices/Embedding.md

@@ -95,3 +95,17 @@ SWIFT provides two scaffold training scripts:
 
 - [GTE Model](https://github.com/tastelikefeet/swift/blob/main/examples/train/embedding/train_gte.sh)
 - [GME Model](https://github.com/tastelikefeet/swift/blob/main/examples/train/embedding/train_gme.sh)
+
+## Inference
+
+SWIFT currently does not support Embedding model inference and deployment (due to time constraints). You can use the original model's code for inference:
+
+https://www.modelscope.cn/models/iic/gte_Qwen2-7B-instruct
+
+https://www.modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct
+
+If you've used other models to train embedding from scratch (for example, the original `qwen2-vl` model + `--task_type embedding`), you can also use gme's inference code, but please note:
+
+https://www.modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct/file/view/master/gme_inference.py?status=1#L111
+
+Please modify the template here to match the model's own template to ensure the final embeddings align correctly. It's particularly important to note that the template for the gme model is different from the chatml template for the `qwen2-vl` or `qwen2.5-vl` series. In its inference code, the ending character is `<|endoftext|>` rather than `<|im_end|>`.