huggingface

Classes:

Name	Description
SGHFLM	Eval harness model class for evaluating Stained Glass Transforms.

SGHFLM

Bases: HFLM

Eval harness model class for evaluating Stained Glass Transforms.

Parameters:

Name	Type	Description	Default
transform_model_path	str	The path to the Stained Glass Transform.	required
apply_stainedglass	bool	Whether to apply the Stained Glass Transform.	required
debug_clean_embeds	bool	Pass clean embeddings to the model, only for debugging purposes.	False
seed	int | None	The seed to use for the Stained Glass Transform.	1234
args	Any	Additional arguments to pass to the parent class.	()
kwargs	Any	Additional keyword arguments to pass to the parent class.	{}

Methods:

Name	Description
apply_chat_template	Return the json string of the chat history which will be processed by _apply_noise_tokenizer_mapper method.
tok_batch_encode	Encode a batch of strings into input ids and attention masks.

apply_chat_template

apply_chat_template(
    chat_history: list[dict[str, str]],
) -> str

Return the json string of the chat history which will be processed by _apply_noise_tokenizer_mapper method.

Parameters:

Name	Type	Description	Default
chat_history	list[dict[str, str]]	The chat history.	required

Returns:

Type	Description
str	The json string of the chat history.

tok_batch_encode

tok_batch_encode(
    strings: list[str],
    padding_side: str = "left",
    left_truncate_len: int | None = None,
    truncation: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]

Encode a batch of strings into input ids and attention masks.

Parameters:

Name	Type	Description	Default
strings	list[str]	The list of strings to encode.	required
padding_side	str	The side to pad the sequences.	'left'
left_truncate_len	int | None	The length to truncate the left side of the sequences.	None
truncation	bool	Whether to truncate the sequences.	False

Returns:

Type	Description
tuple[torch.Tensor, torch.Tensor]	A tuple of input embeddings and attention masks if apply_stainedglass is True, otherwise the input ids and attention masks.