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@gabrielmbmb gabrielmbmb released this 08 Oct 14:53
· 4 commits to main since this release
1.4.0
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✨ Release highlights

Offline Batch Generation and OpenAI Batch API

We’ve updated the LLM interface so now LLMs using an external platform that offers a batch service can be integrated in distilabel. In addition, OpenAILLM has been updated so it can use the OpenAI Batch API to get 50% cost reductions.

distilabel-offline-batch-generation.mp4

Improved cache for maximum outputs reusability

We all know that running LLM is costly and most of the times we want to reuse as much as we can the outputs generated with them. Before this release, distilabel cache mechanism enabled to recover a pipeline execution that was stopped before finishing and to re-create the Distiset generated by one that finished its execution and was re-executed.

In this release, we've greatly improved the cache so the outputs of all the Steps are cached and therefore can be reused in other pipelines executions even if the pipeline has changed:

image

In addition, we've added a use_cache attribute in the Steps that allows toggling the use of the cache at step level.

Steps can generated artifacts

In some cases, Step produces some additional artifacts that are used to generate its outputs. These artifacts can take some time to be generated and they could be reused in the future. That’s why we’ve added a new method called Step.save_artifact that can be called within the step to store artifacts generated by it. The artifacts generated by the Step will also get uploaded to the Hugging Face Hub.

from typing import List, TYPE_CHECKING
from distilabel.steps import GlobalStep, StepInput, StepOutput
import matplotlib.pyplot as plt

if TYPE_CHECKING:
    from distilabel.steps import StepOutput


class CountTextCharacters(GlobalStep):
    @property
    def inputs(self) -> List[str]:
        return ["text"]

    @property
    def outputs(self) -> List[str]:
        return ["text_character_count"]

    def process(self, inputs: StepInput) -> "StepOutput":  # type: ignore
        character_counts = []

        for input in inputs:
            text_character_count = len(input["text"])
            input["text_character_count"] = text_character_count
            character_counts.append(text_character_count)

        # Generate plot with the distribution of text character counts
        plt.figure(figsize=(10, 6))
        plt.hist(character_counts, bins=30, edgecolor="black")
        plt.title("Distribution of Text Character Counts")
        plt.xlabel("Character Count")
        plt.ylabel("Frequency")

        # Save the plot as an artifact of the step
        self.save_artifact(
            name="text_character_count_distribution",
            write_function=lambda path: plt.savefig(path / "figure.png"),
            metadata={"type": "image", "library": "matplotlib"},
        )

        plt.close()

        yield inputs

New Tasks: CLAIR, APIGEN and many more!

  • New CLAIR task: CLAIR uses an AI system to minimally revise a solution A→A´ such that the resulting preference A preferred A’ is much more contrastive and precise.
  • New tasks to replicate APIGen framework: APIGenGenerator, APIGenSemanticChecker, APIGenExecutionChecker. These tasks allow generating datasets like the one presented in the paper: APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets
  • New URIAL task that allows using non-instruct models to generate a response for an instruction.
  • New TextClassification task to make zero-shot text classification based on a predefined but highly customizable prompt.
  • TextClustering, to generate clusters from text and group your generations, discovering labels from your data. Comes with 2 steps to run UMAP and DBSCAN algorithms.
  • Updated TextGeneration to simplify customization of tasks that don’t require further post-processing.

New Steps to sample data in your pipelines and remove duplicates

  • New DataSampler step to sample data from other datasets, which can be useful to inject different examples for few-shot examples in your prompts.
  • New EmbeddingDedup step to remove duplicates based on embeddings and a distance metric.
  • New MinHashDedup step to remove near duplicates from the text based on MinHash and MinHashLSH algorithm.
  • New TruncateTextColumns to truncate the length of your texts using either the character length or the number of tokens based on a tokenizer.
  • New CombineOutputs to combine the outputs of two or more steps into a single output.

Generate text embeddings using vLLM

Extra things

What's Changed

New Contributors

Full Changelog: 1.3.2...1.4.0

Contributors

dameikle, davidberenstein1957, and 4 other contributors
Assets 2
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