EvolQuality¶
Evolve the quality of the responses using an LLM
.
EvolQuality
task is used to evolve the quality of the responses given a prompt,
by generating a new response with a language model. This step implements the evolution
quality task from the paper 'What Makes Good Data for Alignment? A Comprehensive Study of
Automatic Data Selection in Instruction Tuning'.
Attributes¶
-
num_evolutions: The number of evolutions to be performed on the responses.
-
store_evolutions: Whether to store all the evolved responses or just the last one. Defaults to
False
. -
include_original_response: Whether to include the original response within the evolved responses. Defaults to
False
. -
mutation_templates: The mutation templates to be used to evolve the responses.
-
seed: The seed to be set for
numpy
in order to randomly pick a mutation method. Defaults to42
.
Runtime Parameters¶
- seed: The seed to be set for
numpy
in order to randomly pick a mutation method.
Input & Output Columns¶
graph TD
subgraph Dataset
subgraph Columns
ICOL0[instruction]
ICOL1[response]
end
subgraph New columns
OCOL0[evolved_response]
OCOL1[evolved_responses]
OCOL2[model_name]
end
end
subgraph EvolQuality
StepInput[Input Columns: instruction, response]
StepOutput[Output Columns: evolved_response, evolved_responses, model_name]
end
ICOL0 --> StepInput
ICOL1 --> StepInput
StepOutput --> OCOL0
StepOutput --> OCOL1
StepOutput --> OCOL2
StepInput --> StepOutput
Inputs¶
-
instruction (
str
): The instruction that was used to generate theresponses
. -
response (
str
): The responses to be rewritten.
Outputs¶
-
evolved_response (
str
): The evolved response ifstore_evolutions=False
. -
evolved_responses (
List[str]
): The evolved responses ifstore_evolutions=True
. -
model_name (
str
): The name of the LLM used to evolve the responses.
Examples¶
Evolve the quality of the responses given a prompt¶
from distilabel.steps.tasks import EvolQuality
from distilabel.llms.huggingface import InferenceEndpointsLLM
# Consider this as a placeholder for your actual LLM.
evol_quality = EvolQuality(
llm=InferenceEndpointsLLM(
model_id="mistralai/Mistral-7B-Instruct-v0.2",
),
num_evolutions=2,
)
evol_quality.load()
result = next(
evol_quality.process(
[
{"instruction": "common instruction", "response": "a response"},
]
)
)
# result
# [
# {
# 'instruction': 'common instruction',
# 'response': 'a response',
# 'evolved_response': 'evolved response',
# 'model_name': '"mistralai/Mistral-7B-Instruct-v0.2"'
# }
# ]