Distilabel and synthetic data community interviews - the outcomes
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Distilabel and synthetic data community interviews - the outcomes
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We've been doing some interview with community members to understand the needs surrounding synthetic data. Many thanks to the participants. Note that, given they interviewees were sourced from our community, so the results will likely represent that.
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We've been doing some interview with community members to understand the needs surrounding synthetic data. Many thanks to the participants. Note that, given they interviewees were sourced from our community, so the results will likely represent that.
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Things distilabel does well
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Things distilabel does well
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- security and reliability by caching generations and having serializable pipelines.
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- security and reliability by caching generations and having serializable pipelines.
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- scaling up generation by parallelising inference and Anyscale Ray
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- scaling up generation by parallelising inference and Anyscale Ray
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- solid implementations of state of the art research papers
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- solid implementations of state of the art research papers
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Things to improve
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Things to improve
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- communication about the fact we support structured generation
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- communication about the fact we support structured generation
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- customization of existing prompt implementations are difficult
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- customization of existing prompt implementations are difficult
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- creation of new tasks prove difficult
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- creation of new tasks prove difficult
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- arguments and parameters for tasks aren't available at first glance
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- arguments and parameters for tasks aren't available at first glance
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- the learning curve can be steep
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- the learning curve can be steep
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- more tutorials that represent real-life usage
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- more tutorials that represent real-life usage
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Things to note
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Things to note
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- create small scale and large scale dataset to Millions of records
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- create small scale and large scale dataset to Millions of records
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- people use synthetic data to move away from frontier model providers
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- people use synthetic data to move away from frontier model providers
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- people mostly use 7B or 70B models for generating
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- people mostly use 7B or 70B models for generating
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Participate here: 
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Participate here: 
https://github.com/argilla-io/distilabel/issues
https://github.com/argilla-io/distilabel/issues
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Distilabel and synthetic data community interviews - the outcomes

We've been doing some interview with community members to understand the needs surrounding synthetic data. Many thanks to the participants. Note that, given they interviewees were sourced from our community, so the results will likely represent that.

Things distilabel does well
- security and reliability by caching generations and having serializable pipelines.
- scaling up generation by parallelising inference and Anyscale Ray
- solid implementations of state of the art research papers

Things to improve
- communication about the fact we support structured generation
- customization of existing prompt implementations are difficult
- creation of new tasks prove difficult
- arguments and parameters for tasks aren't available at first glance
- the learning curve can be steep
- more tutorials that represent real-life usage

Things to note
- create small scale and large scale dataset to Millions of records
- people use synthetic data to move away from frontier model providers
- people mostly use 7B or 70B models for generating

Participate here: https://github.com/argilla-io/distilabel/issues
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