PEFT models donot "override" user's argument for return_full_text.
Author: SalehAhmad1
Pull request: no

### System Info

Mistral for Causal LM inputs the kwarg argument **"return_full_text"** but does not affect the output response. I am using the following [HF Model](https://huggingface.co/SalehAhmad/Mistral-7B-Instruct-v0.1-JSON-Test_Generation-2-Epoch)

Like this:

```
def infer(user_prompt):
    runtimeFlag = "cuda:0"
    inputs = tokenizer([user_prompt], return_tensors="pt").to(runtimeFlag)
    outputs = model.generate(**inputs, max_new_tokens=2046, kwargs={'stop': ["###Human:"], 'return_full_text':False, 'device_map':"auto"}).to(runtimeFlag)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
```

However, the returned response has the prompt pre-pended to it.

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

```
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
config = PeftConfig.from_pretrained("SalehAhmad/Mistral-7B-Instruct-v0.1-JSON-Test_Generation-2-Epoch", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("SalehAhmad/Mistral-7B-Instruct-v0.1-JSON-Test_Generation-2-Epoch", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(model, "SalehAhmad/Mistral-7B-Instruct-v0.1-JSON-Test_Generation-2-Epoch", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto", return_full_text=False)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

def infer(user_prompt):
    runtimeFlag = "cuda:0"
    inputs = tokenizer([user_prompt], return_tensors="pt").to(runtimeFlag)
    outputs = model.generate(**inputs, max_new_tokens=2046, kwargs={'stop': ["###Human:"], 'return_full_text':False, 'device_map':"auto"}).to(runtimeFlag)
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)


Sys_OBJECTIVE = '''You are a chatbot, who is helping to curate datasets. When given an input context paragraph, you have to generate only one mcq question, 
it's options and it's actual answer. You have to follow the given JSON format for generating the question, options and answer.
Donot use words like "in this paragraph", "from the context" etc. The questions should be independent of any other question.
'''

Sys_SUBJECTIVE = '''You are a chatbot, who is helping to curate datasets. When given an input context paragraph, you have to generate only one subjective quesion,
and it's actual answer. You have to follow the given JSON format for generating the question and answer.
Donot use words like "in this paragraph", "from the context" etc. The questions should be independent of any other question.'''

Prompt = '''And in the leadership styles it will be that is the is the there will be the changing into the leadership styles and in the leadership styles it will be that is the the approach will be for doing this type of the research which has been adopted in this paper is that is the degree of the correlation and its statistical significance between the self-assess leadership behavior and the 360 degree assessment of performance, evidence is presented showing that results vary in different context.'''
 
Formatted_Prompt_OBJECTIVE = f"###Human: {Sys_OBJECTIVE}\nThe context is: {Prompt}\n###Assistant: "

Formatted_Prompt_SUBJECTIVE = f"###Human: {Sys_SUBJECTIVE}\nThe context is: {Prompt}\n###Assistant: "

res = infer(Formatted_Prompt_OBJECTIVE)
print(res)
```

### Expected behavior
I want the input prompt to not get prepended to the response.

Comments:
cc @younesbelkada @ArthurZucker 

Hi @SalehAhmad1 
Thanks for the issue ! 
From what I have checked `return_full_text` needs to be passed to a `pipeline` object and not at the model's init as you tried to do. See: https://github.com/huggingface/transformers/blob/main/docs/source/en/tasks/prompting.md for more details, you can pass a path to a PEFT model directly to the pipeline through the PEFT integration: https://huggingface.co/docs/transformers/peft

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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