Add note about special tokens in chat templates for LoRA SFT

docs/source/sft_trainer.mdx

@@ -331,33 +331,38 @@ Note that all keyword arguments of `from_pretrained()` are supported.
 
 ### Training adapters
 
-We also support tight integration with 🤗 PEFT library so that any user can conveniently train adapters and share them on the Hub instead of training the entire model
+We also support tight integration with 🤗 PEFT library so that any user can conveniently train adapters and share them on the Hub instead of training the entire model.
 
 ```python
 from datasets import load_dataset
 from trl import SFTConfig, SFTTrainer
 from peft import LoraConfig
 
-dataset = load_dataset("stanfordnlp/imdb", split="train")
+dataset = load_dataset("trl-lib/Capybara", split="train")
 
 peft_config = LoraConfig(
     r=16,
     lora_alpha=32,
     lora_dropout=0.05,
-    bias="none",
+    target_modules="all-linear",
+    modules_to_save=["lm_head", "embed_token"],
     task_type="CAUSAL_LM",
 )
 
 trainer = SFTTrainer(
-    "EleutherAI/gpt-neo-125m",
+    "Qwen/Qwen2.5-0.5B",
     train_dataset=dataset,
-    args=SFTConfig(output_dir="/tmp"),
+    args=SFTConfig(output_dir="Qwen2.5-0.5B-SFT"),
     peft_config=peft_config
 )
 
 trainer.train()
 ```
 
+> [!WARNING]
+> If the chat template contains special tokens like `<|im_start|>` (ChatML) or `<|eot_id|>` (Llama), the embedding layer and LM head must be included in the trainable parameters via the `modules_to_save` argument. Without this, the fine-tuned model will produce unbounded or nonsense generations. If the chat template doesn't contain special tokens (e.g. Alpaca), then the `modules_to_save` argument can be ignored or set to `None`.
+
+
 You can also continue training your `PeftModel`. For that, first load a `PeftModel` outside `SFTTrainer` and pass it directly to the trainer without the `peft_config` argument being passed.
 
 ### Training adapters with base 8 bit models