Prompt tuning V2 with BloomZ

[phalexo]

I am not totally certain if it is an issue or not.

The training data from the csv file contains two columns "act" and "prompt" but before this data gets tokenized and passed in for training, the columns are merged into a single column. Why?

I thought one should have "inputs" i.e. the initial short prompt "act as a trainer" and then a separate "labels" column which is the detailed output prompt.

Could you explain the rationale for merging the two columns? Like you see below? How is the training happening if you have a SINGLE input?

def concatenate_columns_prompt(dataset):
    def concatenate(example):
        example['prompt'] = "Act as a {}. Prompt: {}".format(example['act'], example['prompt'])
        return example

    dataset = dataset.map(concatenate)
    return dataset

Thanks.

[peremartra]

Hi @phalexo you are right it is not and issue, but there is a lack of explanations in the notebook, i will try to improve it. And maybe do some different samples.

As I'm using the peft library some work is done inside the libraries, In this case the function DataCollatyorForLanguageModeling helps to prepare the data to train the model.

And you need to take in consideration that with a Transformer architecture in text generation the output is the next word generated. For each word generated the input is the sequence of words preceding it. (change word for tokens).

Hope it helps to clarify how it is working, if not, don't hesitate to ask again.