Updates running moments from batch's moments computed across ranks

This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this

This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of

Generates and yields batches from the reordered array. Parameters: - n (int): The size of each batch. Defaults to 1.

Restores the original order of elements from the reordered list. Parameters: - newarr (List): The reordered array.

r""" Save the pretrained model to a directory. This method is a wrapper around `transformers.PreTrainedModel.save_pretrained`. Please

r""" Return the state_dict of the pretrained model.

Log `logs` on the various objects watching training, including stored metrics. Args: logs (`Dict[str, float]`):

Compute mean of tensor with a masked values.

Returns the tokenizer used for tokenizing text inputs

r""" Post initialization method. This method is called after the model is instantiated and loaded from a checkpoint. It can be used to

Returns the autocast context manager

r""" Instantiates a new model from a pretrained model from `transformers`. The pretrained model is loaded using the `from_pretrained`

Groups elements of an iterable based on a provided function. Parameters: - arr (Iterable): The iterable to be grouped.

Run the environment on a list of queries. Args: queries (list[str]): A list of queries to run the model in the environme

Generate table of results.

:return: Iterable[obj] A iterable of any object, that doc_to_text can handle

:return: Iterable[obj] A iterable of any object, that doc_to_text can handle

Defines the operation to perform on a list of the `inst.resps` properties of `Instance` objects. Should return the list of (filtered)

Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together. We do this to avoid doing two forwar

Generate response with the model given the query tensor. call the `generate` method of the model. Args: query_te

Process an image with variable resolutions. Args: image (PIL.Image.Image): The input image to be processed. processor: The i

Overwrite the `push_to_hub` method in order to force-add the tag "sft" when pushing the model on the Hub. Please refer to `~transform

Rotates half the hidden dims of the input.

Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str,

Flatten dictionary and concatenate nested keys with separator.

Given the results and sample results filenames, extracts and returns the datetime.