preference_dataset¶
- torchtune.datasets.preference_dataset(tokenizer: ModelTokenizer, *, source: str, column_map: Optional[Dict[str, str]] = None, train_on_input: bool = False, new_system_prompt: Optional[str] = None, split: str = 'train', **load_dataset_kwargs: Dict[str, Any]) PreferenceDataset [source]¶
Configures a custom preference dataset comprising interactions between user and model assistant.
This builder function can be used to configure a custom preference dataset directly from the yaml config as an alternative to
PreferenceDataset
, as it is made to be config friendly.This function requires the dataset to have “chosen” and “rejected” columns. A single sample will share an identical system +/ user prompt between both “chosen” and “rejected” columns, followed by one or multiple turns of user and assistant messages:
| chosen | rejected | |----------------------------------------|----------------------------------------| | [{"role": "user", "content": Q1}, | [{"role": "user", "content": Q1}, | | {"role": "assistant", "content": C1}] | {"role": "assistant", "content": R1}] |
This example will be converted to:
chosen_messages = [ Message(role="user", content="Q1"), Message(role="assistant", content="C1"), ] rejected_messages = [ Message(role="user", content="Q1"), Message(role="assistant", content="R1"), ]
These lists of messages are then tokenized for model training. Currently, this function only supports conversations identical to
OpenAIToMessages
, and does not support custom message formats.If your dataset does not follow this format, we recommend creating a custom message transform similar to
ChosenRejectedToMessages
and using it in a custom dataset builder function similar topreference_dataset
.Masking of the prompt during training is controlled by the
train_on_input
flag, which is: set toFalse
by default.If
train_on_input
is True, the prompt is used during training and contributes to the loss.If
train_on_input
is False, the prompt is masked out (tokens replaced with -100).
- Parameters:
tokenizer (ModelTokenizer) – Tokenizer used by the model that implements the
tokenize_messages
method.source (str) – path to dataset repository on Hugging Face. For local datasets, define source as the data file type (e.g. “json”, “csv”, “text”), pass in the filepath in
data_files
, and setsplit="train"
. See Hugging Face’sload_dataset
for more details.column_map (Optional[Dict[str, str]]) – a mapping from the expected columns “chosen” and “rejected” in the message transform
ChosenRejectedToMessages
to the new column names in the dataset. Keys should be “chosen” and “rejected” and values should be the actual column names. If None, keep the default columns “chosen” and “rejected”.train_on_input (bool) – Whether the model is trained on the prompt or not. Default is False.
new_system_prompt (Optional[str]) – if specified, prepend a system message to every sample for both chosen and rejected. 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.
split (str) –
split
argument fordatasets.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
.
Examples:
my_preference_dataset.json [ { "chosen_conversations": [ { "content": "What do I do when I have a hole in my trousers?", "role": "user" }, { "content": "Fix the hole.", "role": "assistant" } ], "rejected_conversations": [ { "content": "What do I do when I have a hole in my trousers?", "role": "user" }, { "content": "Take them off.", "role": "assistant" } ] } ]
>>> from torchtune.datasets import preference_dataset >>> column_map = { ... "chosen": "chosen_conversations", ... "rejected": "rejected_conversations" >>> } >>> dataset = preference_dataset( ... tokenizer=tokenizer, ... source="json", ... column_map=column_map, ... data_files="my_preference_dataset.json", ... train_on_input=False, ... split="train", >>> ) >>> tokenizer.decode(dataset[0]["chosen_input_ids"], skip_special_tokens=True) What do I do when I have a hole in my trousers?Fix the hole. >>> tokenizer.decode(dataset[0]["rejected_input_ids"], skip_special_tokens=True) What do I do when I have a hole in my trousers?Take them off.
This can also be accomplished via the yaml config:
dataset: _component_: torchtune.datasets.preference_dataset source: json data_files: my_preference_dataset.json column_map: chosen: chosen_conversations rejected: rejected_conversations train_on_input: False split: train
- Returns:
The preference dataset built from source paired data.
- Return type: