Tasks for generating and judging with LLMs¶
Working with Tasks¶
The Task is a special kind of Step that includes the LLM as a mandatory argument. As with a Step, it is normally used within a Pipeline but can also be used standalone.
For example, the most basic task is the TextGeneration task, which generates text based on a given instruction.
from distilabel.llms import InferenceEndpointsLLM
from distilabel.steps.tasks import TextGeneration
task = TextGeneration(
name="text-generation",
llm=InferenceEndpointsLLM(
model_id="meta-llama/Meta-Llama-3-70B-Instruct",
tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
),
)
task.load()
next(task.process([{"instruction": "What's the capital of Spain?"}]))
# [
# {
# 'instruction': "What's the capital of Spain?",
# 'generation': 'The capital of Spain is Madrid.',
# 'distilabel_metadata': {
# 'raw_output_text-generation': 'The capital of Spain is Madrid.',
# 'raw_input_text-generation': [
# {'role': 'user', 'content': "What's the capital of Spain?"}
# ]
# },
# 'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
# }
# ]
Note
The Step.load() always needs to be executed when being used as a standalone. Within a pipeline, this will be done automatically during pipeline execution.
As shown above, the TextGeneration task adds a generation based on the instruction.
Tip
Since version 1.2.0, we provide some metadata about the LLM call through distilabel_metadata. This can be disabled by setting the add_raw_output attribute to False when creating the task.
Additionally, since version 1.4.0, the formatted input can also be included, which can be helpful when testing
custom templates (testing the pipeline using the dry_run method).
Task.print¶
Info
New since version 1.4.0, Task.print Task.print method.
The Tasks include a handy method to show what the prompt formatted for an LLM would look like, let's see an example with UltraFeedback, but it applies to any other Task.
from distilabel.steps.tasks import UltraFeedback
from distilabel.llms.huggingface import InferenceEndpointsLLM
uf = UltraFeedback(
llm=InferenceEndpointsLLM(
model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
),
)
uf.load()
uf.print()
The result will be a rendered prompt, with the System prompt (if contained for the task) and the User prompt, rendered with rich (it will show exactly the same in a jupyter notebook).
In case you want to test with a custom input, you can pass an example to the tasksformat_input` method (or generate it on your own depending on the task), and pass it to the print method so that it shows your example:
Using a DummyLLM to avoid loading one
In case you don't want to load an LLM to render the template, you can create a dummy one like the ones we could use for testing.
from distilabel.llms.base import LLM
from distilabel.llms.mixins.magpie import MagpieChatTemplateMixin
class DummyLLM(AsyncLLM, MagpieChatTemplateMixin):
structured_output: Any = None
magpie_pre_query_template: str = "llama3"
def load(self) -> None:
pass
@property
def model_name(self) -> str:
return "test"
def generate(
self, input: "FormattedInput", num_generations: int = 1
) -> "GenerateOutput":
return ["output" for _ in range(num_generations)]
You can use this LLM just as any of the other ones to load your task and call print:
Note
When creating a custom task, the print method will be available by default, but it is limited to the most common scenarios for the inputs. If you test your new task and find it's not working as expected (for example, if your task contains one input consisting of a list of texts instead of a single one), you should override the _sample_input method. You can inspect the UltraFeedback source code for this.
Specifying the number of generations and grouping generations¶
All the Tasks have a num_generations attribute that allows defining the number of generations that we want to have per input. We can update the example above to generate 3 completions per input:
from distilabel.llms import InferenceEndpointsLLM
from distilabel.steps.tasks import TextGeneration
task = TextGeneration(
name="text-generation",
llm=InferenceEndpointsLLM(
model_id="meta-llama/Meta-Llama-3-70B-Instruct",
tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
),
num_generations=3,
)
task.load()
next(task.process([{"instruction": "What's the capital of Spain?"}]))
# [
# {
# 'instruction': "What's the capital of Spain?",
# 'generation': 'The capital of Spain is Madrid.',
# 'distilabel_metadata': {'raw_output_text-generation': 'The capital of Spain is Madrid.'},
# 'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
# },
# {
# 'instruction': "What's the capital of Spain?",
# 'generation': 'The capital of Spain is Madrid.',
# 'distilabel_metadata': {'raw_output_text-generation': 'The capital of Spain is Madrid.'},
# 'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
# },
# {
# 'instruction': "What's the capital of Spain?",
# 'generation': 'The capital of Spain is Madrid.',
# 'distilabel_metadata': {'raw_output_text-generation': 'The capital of Spain is Madrid.'},
# 'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
# }
# ]
In addition, we might want to group the generations in a single output row as maybe one downstream step expects a single row with multiple generations. We can achieve this by setting the group_generations attribute to True:
from distilabel.llms import InferenceEndpointsLLM
from distilabel.steps.tasks import TextGeneration
task = TextGeneration(
name="text-generation",
llm=InferenceEndpointsLLM(
model_id="meta-llama/Meta-Llama-3-70B-Instruct",
tokenizer_id="meta-llama/Meta-Llama-3-70B-Instruct",
),
num_generations=3,
group_generations=True
)
task.load()
next(task.process([{"instruction": "What's the capital of Spain?"}]))
# [
# {
# 'instruction': "What's the capital of Spain?",
# 'generation': ['The capital of Spain is Madrid.', 'The capital of Spain is Madrid.', 'The capital of Spain is Madrid.'],
# 'distilabel_metadata': [
# {'raw_output_text-generation': 'The capital of Spain is Madrid.'},
# {'raw_output_text-generation': 'The capital of Spain is Madrid.'},
# {'raw_output_text-generation': 'The capital of Spain is Madrid.'}
# ],
# 'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
# }
# ]
Defining custom Tasks¶
We can define a custom step by creating a new subclass of the Task and defining the following:
-
inputs: is a property that returns a list of strings with the names of the required input fields or a dictionary in which the keys are the names of the columns and the values are boolean indicating whether the column is required or not. -
format_input: is a method that receives a dictionary with the input data and returns aChatTypefollowing the chat-completion OpenAI message formatting. -
outputs: is a property that returns a list of strings with the names of the output fields or a dictionary in which the keys are the names of the columns and the values are boolean indicating whether the column is required or not. This property should always includemodel_nameas one of the outputs since that's automatically injected from the LLM. -
format_output: is a method that receives the output from theLLMand optionally also the input data (which may be useful to build the output in some scenarios), and returns a dictionary with the output data formatted as needed i.e. with the values for the columns inoutputs. Note that there's no need to include themodel_namein the output.
from typing import Any, Dict, List, Union, TYPE_CHECKING
from distilabel.steps.tasks.base import Task
if TYPE_CHECKING:
from distilabel.steps.typing import StepColumns
from distilabel.steps.tasks.typing import ChatType
class MyCustomTask(Task):
@property
def inputs(self) -> "StepColumns":
return ["input_field"]
def format_input(self, input: Dict[str, Any]) -> "ChatType":
return [
{
"role": "user",
"content": input["input_field"],
},
]
@property
def outputs(self) -> "StepColumns":
return ["output_field", "model_name"]
def format_output(
self, output: Union[str, None], input: Dict[str, Any]
) -> Dict[str, Any]:
return {"output_field": output}