r""" Gets the train dataset and optionally gets the evaluation dataset.

Retrieve a transformer model with a sequence regression head on top. This function loads a pretrained transformer model and attaches a linear lay

r""" Pre-processes token ids after tokenization for VLMs.

r""" Saves model checkpoint. Subclass and override to inject custom behavior.

r""" Set the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas use a different random ordering for

r""" Casts a torch tensor or a numpy array to a numpy array.

r""" Plots loss curves and saves the image.

r""" Collects GPU or NPU memory.

r""" Regularizes images to avoid error. Including reading and pre-processing.

Get responses from the generater. Args: input_list: it contains input texts, each item represents a sample. Returns:

r""" Gets the current available device.

r""" Gets a list of scores of the reward model.

r""" Computes the real sequence length after truncation by the cutoff_len.

r""" Loads user config if exists.

Save the dataset into the original format.

Convert all information within the data sample into a dict. Information generated during the inference will be saved into output field.

r""" Returns a list of names of parameters with weight decay. (weights in non-layernorm layers)

Init model from pretrained checkpoint. Returns: List: list of remote object refs.

Compresses the given context. Args: context (List[str]): List of context strings that form the basis of the prompt.

the front half is matched for spv, the latter half is unmatched for KL

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

r""" Gets the number of available GPU or NPU devices.

r""" Returns the list of all elements.

Resolve and validate max_tokens parameters from both params and generation_params. Args: params: Dictionary containing user-provided