content.raw: Distilabel and synthetic data community interviews - the outcomes
content.type: text
content.value: Distilabel and synthetic data community interviews - the outcomes
content.type: new_line
content.type: new_line
content.raw: 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.
content.type: text
content.value: 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.
content.type: new_line
content.type: new_line
content.raw: Things distilabel does well
content.type: text
content.value: Things distilabel does well
content.type: new_line
content.raw: - security and reliability by caching generations and having serializable pipelines.
content.type: text
content.value: - security and reliability by caching generations and having serializable pipelines.
content.type: new_line
content.raw: - scaling up generation by parallelising inference and Anyscale Ray
content.type: text
content.value: - scaling up generation by parallelising inference and Anyscale Ray
content.type: new_line
content.raw: - solid implementations of state of the art research papers
content.type: text
content.value: - solid implementations of state of the art research papers
content.type: new_line
content.type: new_line
content.raw: Things to improve
content.type: text
content.value: Things to improve
content.type: new_line
content.raw: - communication about the fact we support structured generation
content.type: text
content.value: - communication about the fact we support structured generation
content.type: new_line
content.raw: - customization of existing prompt implementations are difficult
content.type: text
content.value: - customization of existing prompt implementations are difficult
content.type: new_line
content.raw: - creation of new tasks prove difficult
content.type: text
content.value: - creation of new tasks prove difficult
content.type: new_line
content.raw: - arguments and parameters for tasks aren't available at first glance
content.type: text
content.value: - arguments and parameters for tasks aren't available at first glance
content.type: new_line
content.raw: - the learning curve can be steep
content.type: text
content.value: - the learning curve can be steep
content.type: new_line
content.raw: - more tutorials that represent real-life usage
content.type: text
content.value: - more tutorials that represent real-life usage
content.type: new_line
content.type: new_line
content.raw: Things to note
content.type: text
content.value: Things to note
content.type: new_line
content.raw: - create small scale and large scale dataset to Millions of records
content.type: text
content.value: - create small scale and large scale dataset to Millions of records
content.type: new_line
content.raw: - people use synthetic data to move away from frontier model providers
content.type: text
content.value: - people use synthetic data to move away from frontier model providers
content.type: new_line
content.raw: - people mostly use 7B or 70B models for generating
content.type: text
content.value: - people mostly use 7B or 70B models for generating
content.type: new_line
content.type: new_line
content.raw: Participate here: 
content.type: text
content.value: Participate here: 
content.href: https://github.com/argilla-io/distilabel/issues
content.raw: https://github.com/argilla-io/distilabel/issues
content.type: link
rawContent: 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
reactions.reaction: 🤗
reactions.users: Tonic osanseviero gabrielmbmb fynnkroeger
reactions.reaction: ❤️
reactions.users: Tonic Obenlia
reactions.reaction: 🧠
reactions.users: Tonic John6666
reactions.reaction: 🚀
reactions.users: Tonic