Magpie¶

Generates conversations using an instruct fine-tuned LLM.

Magpie is a neat method that allows generating user instructions with no seed data or specific system prompt thanks to the autoregressive capabilities of the instruct fine-tuned LLMs. As they were fine-tuned using a chat template composed by a user message and a desired assistant output, the instruct fine-tuned LLM learns that after the pre-query or pre-instruct tokens comes an instruction. If these pre-query tokens are sent to the LLM without any user message, then the LLM will continue generating tokens as if it was the user. This trick allows "extracting" instructions from the instruct fine-tuned LLM. After this instruct is generated, it can be sent again to the LLM to generate this time an assistant response. This process can be repeated N times allowing to build a multi-turn conversation. This method was described in the paper 'Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing'.

Attributes¶

n_turns: the number of turns that the generated conversation will have. Defaults to 1.
end_with_user: whether the conversation should end with a user message. Defaults to False.
include_system_prompt: whether to include the system prompt used in the generated conversation. Defaults to False.
only_instruction: whether to generate only the instruction. If this argument is True, then n_turns will be ignored. Defaults to False.
system_prompt: an optional system prompt, or a list of system prompts from which a random one will be chosen, or a dictionary of system prompts from which a random one will be choosen, or a dictionary of system prompts with their probability of being chosen. The random system prompt will be chosen per input/output batch. This system prompt can be used to guide the generation of the instruct LLM and steer it to generate instructions of a certain topic. Defaults to None.

Runtime Parameters¶

n_turns: the number of turns that the generated conversation will have. Defaults to 1.
end_with_user: whether the conversation should end with a user message. Defaults to False.
include_system_prompt: whether to include the system prompt used in the generated conversation. Defaults to False.
only_instruction: whether to generate only the instruction. If this argument is True, then n_turns will be ignored. Defaults to False.
system_prompt: an optional system prompt, or a list of system prompts from which a random one will be chosen, or a dictionary of system prompts from which a random one will be choosen, or a dictionary of system prompts with their probability of being chosen. The random system prompt will be chosen per input/output batch. This system prompt can be used to guide the generation of the instruct LLM and steer it to generate instructions of a certain topic.

Input & Output Columns¶

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[system_prompt]
        end
        subgraph New columns
            OCOL0[conversation]
            OCOL1[instruction]
            OCOL2[response]
            OCOL3[system_prompt_key]
            OCOL4[model_name]
        end
    end

    subgraph Magpie
        StepInput[Input Columns: system_prompt]
        StepOutput[Output Columns: conversation, instruction, response, system_prompt_key, model_name]
    end

    ICOL0 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepOutput --> OCOL2
    StepOutput --> OCOL3
    StepOutput --> OCOL4
    StepInput --> StepOutput

Inputs¶

system_prompt (str, optional): an optional system prompt that can be provided to guide the generation of the instruct LLM and steer it to generate instructions of certain topic.

Outputs¶

conversation (ChatType): the generated conversation which is a list of chat items with a role and a message. Only if only_instruction=False.
instruction (str): the generated instructions if only_instruction=True or n_turns==1.
response (str): the generated response if n_turns==1.
system_prompt_key (str, optional): the key of the system prompt used to generate the conversation or instruction. Only if system_prompt is a dictionary.
model_name (str): The model name used to generate the conversation or instruction.

Examples¶

Generating instructions with Llama 3 8B Instruct and TransformersLLM¶

from distilabel.llms import TransformersLLM
from distilabel.steps.tasks import Magpie

magpie = Magpie(
    llm=TransformersLLM(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        magpie_pre_query_template="llama3",
        generation_kwargs={
            "temperature": 1.0,
            "max_new_tokens": 64,
        },
        device="mps",
    ),
    only_instruction=True,
)

magpie.load()

result = next(
    magpie.process(
        inputs=[
            {
                "system_prompt": "You're a math expert AI assistant that helps students of secondary school to solve calculus problems."
            },
            {
                "system_prompt": "You're an expert florist AI assistant that helps user to erradicate pests in their crops."
            },
        ]
    )
)
# [
#     {'instruction': "That's me! I'd love some help with solving calculus problems! What kind of calculation are you most effective at? Linear Algebra, derivatives, integrals, optimization?"},
#     {'instruction': 'I was wondering if there are certain flowers and plants that can be used for pest control?'}
# ]

Generating conversations with Llama 3 8B Instruct and TransformersLLM¶

from distilabel.llms import TransformersLLM
from distilabel.steps.tasks import Magpie

magpie = Magpie(
    llm=TransformersLLM(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        magpie_pre_query_template="llama3",
        generation_kwargs={
            "temperature": 1.0,
            "max_new_tokens": 256,
        },
        device="mps",
    ),
    n_turns=2,
)

magpie.load()

result = next(
    magpie.process(
        inputs=[
            {
                "system_prompt": "You're a math expert AI assistant that helps students of secondary school to solve calculus problems."
            },
            {
                "system_prompt": "You're an expert florist AI assistant that helps user to erradicate pests in their crops."
            },
        ]
    )
)
# [
#     {
#         'conversation': [
#             {'role': 'system', 'content': "You're a math expert AI assistant that helps students of secondary school to solve calculus problems."},
#             {
#                 'role': 'user',
#                 'content': 'I'm having trouble solving the limits of functions in calculus. Could you explain how to work with them? Limits of functions are denoted by lim x→a f(x) or lim x→a [f(x)]. It is read as "the limit as x approaches a of f
# of x".'
#             },
#             {
#                 'role': 'assistant',
#                 'content': 'Limits are indeed a fundamental concept in calculus, and understanding them can be a bit tricky at first, but don't worry, I'm here to help! The notation lim x→a f(x) indeed means "the limit as x approaches a of f of
# x". What it's asking us to do is find the'
#             }
#         ]
#     },
#     {
#         'conversation': [
#             {'role': 'system', 'content': "You're an expert florist AI assistant that helps user to erradicate pests in their crops."},
#             {
#                 'role': 'user',
#                 'content': "As a flower shop owner, I'm noticing some unusual worm-like creatures causing damage to my roses and other flowers. Can you help me identify what the problem is? Based on your expertise as a florist AI assistant, I think it
# might be pests or diseases, but I'm not sure which."
#             },
#             {
#                 'role': 'assistant',
#                 'content': "I'd be delighted to help you investigate the issue! Since you've noticed worm-like creatures damaging your roses and other flowers, I'll take a closer look at the possibilities. Here are a few potential culprits: 1.
# **Aphids**: These small, soft-bodied insects can secrete a sticky substance called"
#             }
#         ]
#     }
# ]

References¶

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing