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llmcompressor.transformers.finetune.data.custom

Custom dataset implementation for JSON and CSV data sources.

This module provides a CustomDataset class for loading and processing local JSON and CSV files for text generation fine-tuning. Supports flexible data formats and custom preprocessing pipelines for user-provided datasets.

Classes:

  • CustomDataset –

    Child text generation class for custom local dataset supporting load

CustomDataset 

CustomDataset(
    dataset_args: DatasetArguments,
    split: str,
    processor: Processor,
)

Bases: TextGenerationDataset

Child text generation class for custom local dataset supporting load for csv and json

Parameters:

  • dataset_args

    (DatasetArguments) –

    configuration settings for dataset loading

  • split

    (str) –

    split from dataset to load, for instance test or train[:5%] Can also be set to None to load all the splits

  • processor

    (Processor) –

    processor or tokenizer to use on dataset

Source code in llmcompressor/transformers/finetune/data/base.py 
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def __init__(
    self,
    dataset_args: DatasetArguments,
    split: str,
    processor: Processor,
):
    self.dataset_args = dataset_args
    self.split = split
    self.processor = processor

    # get tokenizer
    self.tokenizer = getattr(self.processor, "tokenizer", self.processor)

    if self.tokenizer is not None:
        # fill in pad token
        if not self.tokenizer.pad_token:
            self.tokenizer.pad_token = self.tokenizer.eos_token

        # configure sequence length
        max_seq_length = dataset_args.max_seq_length
        if dataset_args.max_seq_length > self.tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({max_seq_length}) is larger than "
                f"maximum length for model ({self.tokenizer.model_max_length}). "
                f"Using max_seq_length={self.tokenizer.model_max_length}."
            )
        self.max_seq_length = min(
            dataset_args.max_seq_length, self.tokenizer.model_max_length
        )

        # configure padding
        self.padding = (
            False
            if self.dataset_args.concatenate_data
            else "max_length"
            if self.dataset_args.pad_to_max_length
            else False
        )

    else:
        self.max_seq_length = None
        self.padding = False