the_cauldron_dataset

torchtune.datasets.the_cauldron_dataset(model_transform: Transform, *, subset: str, source: str = 'HuggingFaceM4/the_cauldron', column_map: Optional[Dict[str, str]] = None, new_system_prompt: Optional[str] = None, train_on_input: bool = True, packed: bool = False, split: str = 'train', **load_dataset_kwargs: Dict[str, Any]) → Union[SFTDataset, PackedDataset]

Support for family of image + text datasets similar to The Cauldron from Hugging Face Datasets.

The Cauldron consists of numerous datasets. You must specify one of the datasets using the subset argument.

The model transform is expected to be a callable that applies pre-processing steps specific to a model. For multimodal datasets, this is expected to be at minimum a tokenizer and an image transform. The tokenizer will convert text sequences into token IDs after the dataset is converted to a list of Message. The image transform will load the image and process it in accordance to the model’s requirements.

Here is a minimal example for illustrative purposes:

from torchtune.models.llama3 import llama3_tokenizer
from torchtune.models.clip import CLIPImageTransform
from torchtune.modules.transforms import Transform

class MyModelTransform(Transform):
    def __init__(
        self,
        tokenizer_path: str,
        max_seq_len: Optional[int] = None,
    ):
        self.tokenizer = llama3_tokenizer(tokenizer_path)
        self.image_transform = CLIPImageTransform()

    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
        tokens, mask = self.tokenizer.tokenize_messages(sample["messages"])
        images = self.image_transform(sample["images"])
        return {
            "tokens": tokens,
            "mask": mask,
            "images": images,
        }

See SFTDataset for more details about model transforms and message transforms.

Parameters:

• model_transform (Transform) – model-specific transform class that takes in a sample dict and applies custom transforms on the keys. It should consist of at minimum two components: text tokenization (called on the “messages” field) and image transform (called on the “images” field). The keys returned by the model transform should be aligned with the expected inputs into the model.

• subset (str) – name of the subset of the dataset to load. See the dataset card for options.

• source (str) – path to dataset repository on Hugging Face. For local datasets, define source as the data file type (e.g. “json”, “csv”, “text”) and pass in the filepath in data_files. See Hugging Face’s load_dataset for more details. Default is HuggingFaceM4/the_cauldron.

• column_map (Optional[Dict[str, str]]) – a mapping to change the expected “images” and “texts” column names to the actual column names in the dataset. Default is None, keeping the default column names.

• new_system_prompt (Optional[str]) – if specified, prepend a system message. This can serve as instructions to guide the model response. Setting this will OVERRIDE any system messages already present in the dataset. Default is None.

• train_on_input (bool) – Whether the model is trained on the prompt or not. Default is False.

• packed (bool) – Whether or not to pack the dataset to max_seq_len prior to training. Default is False.

• split (str) – split argument for datasets.load_dataset. You can use this argument to load a subset of a given split, e.g. split="train[:10%]". Default is “train”.

• **load_dataset_kwargs (Dict[str, Any]) – additional keyword arguments to pass to load_dataset. See Hugging Face’s API ref for more details.

Returns:

dataset configured with source data and transform

Return type:

Union[SFTDataset, PackedDataset]

Raises:

ValueError – If packed is True and max_seq_len is not set on the model_transform.

Example

>>> cauldron_ds = the_cauldron_dataset(model_transform=model_transform, subset="ai2d")
>>> for batch in Dataloader(cauldron_ds, batch_size=8):
>>>     print(f"Batch size: {len(batch)}")
>>> Batch size: 8