Avoid validation error on duplicated attribute

`pad_token_id` no longer needs to be passed explicitly. `generate_kwargs` dictionary already contains it. Python does not know which one to pick.

`TypeError: transformers.generation.utils.GenerationMixin.generate() got multiple values for keyword argument 'pad_token_id'`

Removing the attribute override fixes the error.

### Script

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from instruct_pipeline import InstructionTextGenerationPipeline

tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left")
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16)

generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)

res = generate_text("Explain to me the difference between nuclear fission and fusion.")
```

### Error

```
Traceback (most recent call last):
File "/home/alexis/Documents/python/MLU/MLU-FT-LLMS-LAB/script.py", line 10, in <module>
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/alexis/.conda/envs/mlu-ft-llms/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1242, in __call__
return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/alexis/.conda/envs/mlu-ft-llms/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1249, in run_single
model_outputs = self.forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/alexis/.conda/envs/mlu-ft-llms/lib/python3.12/site-packages/transformers/pipelines/base.py", line 1149, in forward
model_outputs = self._forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/alexis/Documents/python/MLU/MLU-FT-LLMS-LAB/instruct_pipeline.py", line 125, in _forward
generated_sequence = self.model.generate(
^^^^^^^^^^^^^^^^^^^^
TypeError: transformers.generation.utils.GenerationMixin.generate() got multiple values for keyword argument 'pad_token_id'
```

instruct_pipeline.py

@@ -22,10 +22,8 @@ INTRO_BLURB = (
 # This is the prompt that is used for generating responses using an already trained model.  It ends with the response
 # key, where the job of the model is to provide the completion that follows it (i.e. the response itself).
 PROMPT_FOR_GENERATION_FORMAT = """{intro}
-
 {instruction_key}
 {instruction}
-
 {response_key}
 """.format(
     intro=INTRO_BLURB,
@@ -37,17 +35,13 @@ PROMPT_FOR_GENERATION_FORMAT = """{intro}
 
 def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int:
     """Gets the token ID for a given string that has been added to the tokenizer as a special token.
-
     When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are
     treated specially and converted to a single, new token.  This retrieves the token ID each of these keys map to.
-
     Args:
         tokenizer (PreTrainedTokenizer): the tokenizer
         key (str): the key to convert to a single token
-
     Raises:
         RuntimeError: if more than one ID was generated
-
     Returns:
         int: the token ID for the given key
     """
@@ -62,7 +56,6 @@ class InstructionTextGenerationPipeline(Pipeline):
         self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
     ):
         """Initialize the pipeline
-
         Args:
             do_sample (bool, optional): Whether or not to use sampling. Defaults to True.
             max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
@@ -132,7 +125,6 @@ class InstructionTextGenerationPipeline(Pipeline):
         generated_sequence = self.model.generate(
             input_ids=input_ids.to(self.model.device),
             attention_mask=attention_mask.to(self.model.device) if attention_mask is not None else None,
-            pad_token_id=self.tokenizer.pad_token_id,
             **generate_kwargs,
         )
 
@@ -209,4 +201,4 @@ class InstructionTextGenerationPipeline(Pipeline):
 
             records.append(rec)
 
-        return records
+        return records