Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

r""" Args: labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing

Get the attributions for (part of) the response. Arguments: start_idx (int, optional): Start index of th

Get the attributions for (part of) the response. Arguments: start_idx (int, optional): Start index of th

Get a version of the context ablated according to the given mask.

Given a file, get the size of file in MB

Load checkpoint as specified in the config file. If load_finetuned is True, load the finetuned model; otherwise, load the pretrained

Example >>> dataset = load_dataset("coco_caption", cfg=None) >>> splits = dataset.keys() >>> print([len(dataset[split]) for split in

Common i/o utility to handle loading data from various file formats. Supported: .pkl, .pickle, .npy, .json For the npy files, we

Load supported models. To list all available models and types in registry: >>> from minigpt4.models import model_zoo >>> print(model

Load model and its related preprocessors. List all available models and types in registry: >>> from minigpt4.models import model_zoo

Example >>> processor = load_processor("alpro_video_train", cfg=None)

A property to get and create learning rate scheduler by split just in need.

The number of sources within the context. I.e., the number of sources that the context is partitioned into. Returns:

The number of sources within the context. I.e., the number of sources that the context is partitioned into. Returns:

The number of sources.

The number of sources within the context. I.e., the number of sources that the context is partitioned into. Returns:

same output as PIL.ImageOps.posterize

r"""Register a dataset builder to registry with key 'name' Args: name: Key with which the builder will be registered. Us

r"""Register a model to registry with key 'name' Args: name: Key with which the task will be registered. Usage:

r"""Register a task to registry with key 'name' Args: name: Key with which the task will be registered. Usage:

r"""Register a processor to registry with key 'name' Args: name: Key with which the task will be registered. Usage:

r"""Register a model to registry with key 'name' Args: name: Key with which the task will be registered. Usage:

r"""Register a task to registry with key 'name' Args: name: Key with which the task will be registered. Usage: