FormatChatGenerationDPO

Format the output of a combination of a ChatGeneration + a preference task for Direct Preference Optimization (DPO).

FormatChatGenerationDPO is a Step that formats the output of the combination of a ChatGeneration task with a preference Task i.e. a task generating ratings such as UltraFeedback following the standard formatting from frameworks such as axolotl or alignment-handbook., so that those are used to rank the existing generations and provide the chosen and rejected generations based on the ratings.

Note

The messages column should contain at least one message from the user, the generations column should contain at least two generations, the ratings column should contain the same number of ratings as generations.

Input & Output Columns

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[messages]
            ICOL1[generations]
            ICOL2[generation_models]
            ICOL3[ratings]
        end
        subgraph New columns
            OCOL0[prompt]
            OCOL1[prompt_id]
            OCOL2[chosen]
            OCOL3[chosen_model]
            OCOL4[chosen_rating]
            OCOL5[rejected]
            OCOL6[rejected_model]
            OCOL7[rejected_rating]
        end
    end

    subgraph FormatChatGenerationDPO
        StepInput[Input Columns: messages, generations, generation_models, ratings]
        StepOutput[Output Columns: prompt, prompt_id, chosen, chosen_model, chosen_rating, rejected, rejected_model, rejected_rating]
    end

    ICOL0 --> StepInput
    ICOL1 --> StepInput
    ICOL2 --> StepInput
    ICOL3 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepOutput --> OCOL3
    StepOutput --> OCOL4
    StepOutput --> OCOL5
    StepOutput --> OCOL6
    StepOutput --> OCOL7
    StepInput --> StepOutput

Inputs

• messages (List[Dict[str, str]]): The conversation messages.

• generations (List[str]): The generations produced by the LLM.

• generation_models (List[str], optional): The model names used to generate the generations, only available if the model_name from the ChatGeneration task/s is combined into a single column named this way, otherwise, it will be ignored.

• ratings (List[float]): The ratings for each of the generations, produced by a preference task such as UltraFeedback.

Outputs

• prompt (str): The user message used to generate the generations with the LLM.

• prompt_id (str): The SHA256 hash of the prompt.

• chosen (List[Dict[str, str]]): The chosen generation based on the ratings.

• chosen_model (str, optional): The model name used to generate the chosen generation, if the generation_models are available.

• chosen_rating (float): The rating of the chosen generation.

• rejected (List[Dict[str, str]]): The rejected generation based on the ratings.

• rejected_model (str, optional): The model name used to generate the rejected generation, if the generation_models are available.

• rejected_rating (float): The rating of the rejected generation.

Examples

Format your dataset for DPO fine tuning

from distilabel.steps import FormatChatGenerationDPO

format_dpo = FormatChatGenerationDPO()
format_dpo.load()

# NOTE: "generation_models" can be added optionally.
result = next(
    format_dpo.process(
        [
            {
                "messages": [{"role": "user", "content": "What's 2+2?"}],
                "generations": ["4", "5", "6"],
                "ratings": [1, 0, -1],
            }
        ]
    )
)
# >>> result
# [
#     {
#         'messages': [{'role': 'user', 'content': "What's 2+2?"}],
#         'generations': ['4', '5', '6'],
#         'ratings': [1, 0, -1],
#         'prompt': "What's 2+2?",
#         'prompt_id': '7762ecf17ad41479767061a8f4a7bfa3b63d371672af5180872f9b82b4cd4e29',
#         'chosen': [{'role': 'user', 'content': "What's 2+2?"}, {'role': 'assistant', 'content': '4'}],
#         'chosen_rating': 1,
#         'rejected': [{'role': 'user', 'content': "What's 2+2?"}, {'role': 'assistant', 'content': '6'}],
#         'rejected_rating': -1
#     }
# ]