[doc] fix bugs in how_to_hack_models.md (#38198)

fix several bugs

docs/source/en/how_to_hack_models.md

@@ -90,11 +90,6 @@ class SamVisionAttentionSplit(SamVisionAttention, nn.Module):
 
         attn_weights = (query * self.scale) @ key.transpose(-2, -1)
 
-        if self.use_rel_pos:
-            attn_weights = self.add_decomposed_rel_pos(
-                attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
-            )
-
         attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
         attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
         attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
@@ -114,13 +109,14 @@ Load the model with [`~PreTrainedModel.from_pretrained`].
 
 ```py
 from transformers import SamModel
-from transformers.models.sam import modeling_sam
-
-# replace the attention class in the modeling_sam module
-modeling_sam.SamVisionAttention = SamVisionAttentionSplit
 
 # load the pretrained SAM model
 model = SamModel.from_pretrained("facebook/sam-vit-base")
+
+# replace the attention class in the vision_encoder module
+for layer in model.vision_encoder.layers:
+    if hasattr(layer, "attn"):
+        layer.attn = SamVisionAttentionSplit(model.config.vision_config, model.config.vision_config.window_size)
 ```
 
 ## LoRA
@@ -138,7 +134,7 @@ config = LoraConfig(
     # apply LoRA to q and v
     target_modules=["q", "v"],
     lora_dropout=0.1,
-    task_type="mask-generation"
+    task_type="FEATURE_EXTRACTION"
 )
 ```
 
@@ -152,5 +148,5 @@ Call [print_trainable_parameters](https://huggingface.co/docs/peft/package_refer
 
 ```py
 model.print_trainable_parameters()
-"trainable params: 608,256 || all params: 94,343,728 || trainable%: 0.6447"
+"trainable params: 589,824 || all params: 94,274,096 || trainable%: 0.6256"
 ```