oumi.datasets.grpo

oumi.datasets.grpo#

GRPO datasets module.

class oumi.datasets.grpo.BerryBenchGrpoDataset(*, dataset_name: str | None = None, dataset_path: str | None = None, split: str | None = None, **kwargs)[source]#

Bases: BaseExperimentalGrpoDataset

Dataset class for the oumi-ai/berrybench-v0.1.1 dataset.

A sample from the dataset: {

“messages”: [
{

“content”: “Return a JSON object showing the frequency of each character in the word ‘黒い’. Only include characters that appear in the word.”, “role”: “user”,

}

], “metadata”: {

“character_count”: 2, “difficulty”: 3, “expected_response”: ‘{”\u9ed2”: 1, “\u3044”: 1}’, “language”: “japanese”, “word”: “黒い”,

},

}

dataset_name: str#
default_dataset: str | None = 'oumi-ai/berrybench-v0.1.1'#
transform(sample: Series) → dict[source]#

Transform the sample into Python dict.

transform_conversation(sample: Series) → Conversation[source]#

Converts the input sample to a Conversation.

Parameters:

sample (dict) – The input example.

Returns:

The resulting conversation.

Return type:

Conversation

trust_remote_code: bool#
class oumi.datasets.grpo.CountdownGrpoDataset(*, dataset_name: str | None = None, dataset_path: str | None = None, split: str | None = None, **kwargs)[source]#

Bases: BaseExperimentalGrpoDataset

Dataset class for the d1shs0ap/countdown dataset.

A sample from the dataset: {“target”: 87, “nums”: [79, 8]}

dataset_name: str#
default_dataset: str | None = 'd1shs0ap/countdown'#
transform(sample: Series) → dict[source]#

Validate and transform the sample into Python dict.

transform_conversation(sample: Series) → Conversation[source]#

Validate and transform the sample into Python dict.

trust_remote_code: bool#
class oumi.datasets.grpo.Gsm8kGrpoDataset(*, dataset_name: str | None = None, dataset_path: str | None = None, split: str | None = None, **kwargs)[source]#

Bases: BaseExperimentalGrpoDataset

Dataset class for the openai/gsm8k dataset.

A sample from the dataset: {

“question”: “Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?”, “answer”: “Natalia sold 48/2 = <<48/2=24>>24 clips in May.

Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May. #### 72”

}

dataset_name: str#
default_dataset: str | None = 'openai/gsm8k'#
transform(sample: Series) → dict[source]#

Validate and transform the sample into Python dict.

transform_conversation(sample: Series) → Conversation[source]#

Validate and transform the sample into Python dict.

trust_remote_code: bool#
class oumi.datasets.grpo.LetterCountGrpoDataset(*, dataset_name: str | None = None, dataset_path: str | None = None, split: str | None = None, **kwargs)[source]#

Bases: BaseExperimentalGrpoDataset

Dataset class for the oumi-ai/oumi-letter-count dataset.

A sample from the dataset: {

“conversation_id”: “oumi_letter_count_0”, “messages”: [

{

“content”: “Can you let me know how many ‘r’s are in ‘pandered’?”, “role”: “user”,

}

], “metadata”: {

“letter”: “r”, “letter_count_integer”: 1, “letter_count_string”: “one”, “unformatted_prompt”: “Can you let me know how many {letter}s are in {word}?”, “word”: “pandered”,

},

}

dataset_name: str#
default_dataset: str | None = 'oumi-ai/oumi-letter-count'#
transform(sample: Series) → dict[source]#

Validate and transform the sample into Python dict.

transform_conversation(sample: Series) → Conversation[source]#

Converts the input sample to a Conversation.

Parameters:

sample (dict) – The input example.

Returns:

The resulting conversation.

Return type:

Conversation

trust_remote_code: bool#
class oumi.datasets.grpo.TldrGrpoDataset(*, dataset_name: str | None = None, dataset_path: str | None = None, split: str | None = None, **kwargs)[source]#

Bases: BaseExperimentalGrpoDataset

Dataset class for the trl-lib/tldr dataset.

dataset_name: str#
default_dataset: str | None = 'trl-lib/tldr'#
trust_remote_code: bool#

Subpackages#

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