Fix code example in quicktour.md (#1181)

docs/source/quicktour.md

@@ -89,23 +89,24 @@ This only saves the incremental 🤗 PEFT weights that were trained, meaning it
 Easily load your model for inference using the [`~transformers.PreTrainedModel.from_pretrained`] function:
 
 ```diff
-  from transformers import AutoModelForSeq2SeqLM
+  from transformers import AutoModelForCausalLM, AutoTokenizer
 + from peft import PeftModel, PeftConfig
 
-+ peft_model_id = "smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM"
++ peft_model_id = "merve/Mistral-7B-Instruct-v0.2"
 + config = PeftConfig.from_pretrained(peft_model_id)
-  model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
+  model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
 + model = PeftModel.from_pretrained(model, peft_model_id)
   tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 
   model = model.to(device)
   model.eval()
-  inputs = tokenizer("Tweet text : @HondaCustSvc Your customer service has been horrible during the recall process. I will never purchase a Honda again. Label :", return_tensors="pt")
+  inputs = tokenizer("Tell me the recipe for chocolate chip cookie", return_tensors="pt")
 
   with torch.no_grad():
       outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=10)
       print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])
-  'complaint'
+  'Tell me the recipe for chocolate chip cookie dough.
+  1. Preheat oven'
 ```
 
 ## Easy loading with Auto classes 
@@ -146,4 +147,4 @@ peft_model_id = "smangrul/openai-whisper-large-v2-LORA-colab"
 
 Now that you've seen how to train a model with one of the 🤗 PEFT methods, we encourage you to try out some of the other methods like prompt tuning. The steps are very similar to the ones shown in this quickstart; prepare a [`PeftConfig`] for a 🤗 PEFT method, and use the `get_peft_model` to create a [`PeftModel`] from the configuration and base model. Then you can train it however you like!
 
-Feel free to also take a look at the task guides if you're interested in training a model with a 🤗 PEFT method for a specific task such as semantic segmentation, multilingual automatic speech recognition, DreamBooth, and token classification.
+Feel free to also take a look at the task guides if you're interested in training a model with a 🤗 PEFT method for a specific task such as semantic segmentation, multilingual automatic speech recognition, DreamBooth, and token classification.