content.raw: 🦫 We have just released 
content.type: text
content.value: 🦫 We have just released 
content.raw: https://huggingface.co/datasets/argilla/Capybara-Preferences
content.id: argilla/Capybara-Preferences
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/argilla/Capybara-Preferences
content.raw:  in collaboration with Kaist AI (
content.type: text
content.value:  in collaboration with Kaist AI (
content.raw: @JW17
content.type: mention
content.user: JW17
content.raw: , 
content.type: text
content.value: , 
content.raw: @nlee-208
content.type: mention
content.user: nlee-208
content.raw: ) and Hugging Face (
content.type: text
content.value: ) and Hugging Face (
content.raw: @lewtun
content.type: mention
content.user: lewtun
content.raw: )
content.type: text
content.value: )
content.type: new_line
content.type: new_line
content.raw: A new synthetic preference dataset built using 
content.type: text
content.value: A new synthetic preference dataset built using 
content.code: distilabel
content.raw: `distilabel`
content.type: inline_code
content.raw:  on top of the awesome 
content.type: text
content.value:  on top of the awesome 
content.raw: https://huggingface.co/datasets/LDJnr/Capybara
content.id: LDJnr/Capybara
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/LDJnr/Capybara
content.raw:  from 
content.type: text
content.value:  from 
content.raw: @LDJnr
content.type: mention
content.user: LDJnr
content.type: text
content.type: new_line
content.type: new_line
content.raw: The current dataset combines the already generated alternative completions from 
content.type: text
content.value: The current dataset combines the already generated alternative completions from 
content.raw: https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized
content.id: argilla/distilabel-capybara-dpo-7k-binarized
content.type: dataset
content.type: resource
content.url: https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized
content.raw: , while also adding the remaining ones using the same approach!
content.type: text
content.value: , while also adding the remaining ones using the same approach!
content.type: new_line
content.type: new_line
content.raw: Here are some key features on how we built it:
content.type: text
content.value: Here are some key features on how we built it:
content.type: new_line
content.type: new_line
content.raw: - 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing
content.type: text
content.value: - 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing
content.type: new_line
content.type: new_line
content.raw: - 🤖 Generation of alternative completions for the existing conversations (last turn only) with: 
content.type: text
content.value: - 🤖 Generation of alternative completions for the existing conversations (last turn only) with: 
content.raw: https://huggingface.co/mlabonne/NeuralBeagle14-7B
content.id: mlabonne/NeuralBeagle14-7B
content.type: model
content.type: resource
content.url: https://huggingface.co/mlabonne/NeuralBeagle14-7B
content.raw: , 
content.type: text
content.value: , 
content.raw: https://huggingface.co/argilla/notus-7b-v1
content.id: argilla/notus-7b-v1
content.type: model
content.type: resource
content.url: https://huggingface.co/argilla/notus-7b-v1
content.raw: , and 
content.type: text
content.value: , and 
content.raw: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
content.id: teknium/OpenHermes-2.5-Mistral-7B
content.type: model
content.type: resource
content.url: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
content.type: new_line
content.type: new_line
content.raw: - 👨🏻‍🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses
content.type: text
content.value: - 👨🏻‍🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses
content.type: new_line
content.type: new_line
content.raw: - 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!
content.type: text
content.value: - 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!
content.type: new_line
content.type: new_line
content.raw: Sounds simple right? Start building your own synthetic datasets with 
content.type: text
content.value: Sounds simple right? Start building your own synthetic datasets with 
content.href: https://github.com/argilla-io/distilabel
content.raw: https://github.com/argilla-io/distilabel
content.type: link
content.raw:  already!
content.type: text
content.value:  already!
rawContent: 🦫 We have just released https://huggingface.co/datasets/argilla/Capybara-Preferences in collaboration with Kaist AI (@JW17, @nlee-208) and Hugging Face (@lewtun)
  
  A new synthetic preference dataset built using `distilabel` on top of the awesome https://huggingface.co/datasets/LDJnr/Capybara from @LDJnr 
  
  The current dataset combines the already generated alternative completions from https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized, while also adding the remaining ones using the same approach!
  
  Here are some key features on how we built it:
  
  - 🧹 Duplicate removal, keeping the conversation besides the last assistant response, and some slight pre-processing
  
  - 🤖 Generation of alternative completions for the existing conversations (last turn only) with: https://huggingface.co/mlabonne/NeuralBeagle14-7B, https://huggingface.co/argilla/notus-7b-v1, and https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B
  
  - 👨🏻‍🏫 Running UltraFeedback via GPT-4 to generate the critique i.e. ratings and rationales, for the last assistant responses
  
  - 🎉 Finally, we selected the chosen and rejected responses based on their UltraFeedback score, and applied some slight post-processing!
  
  Sounds simple right? Start building your own synthetic datasets with https://github.com/argilla-io/distilabel already!
reactions.reaction: 🔥
reactions.users: lunarflu davanstrien tomaarsen qubvel-hf clem alielfilali01 AtAndDev gabrielmbmb
reactions.reaction: 👍
reactions.users: mikeysilva808 AtAndDev gabrielmbmb