Create synthetic instruction datasets using open source LLM's and bonito🐟!
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Create synthetic instruction datasets using open source LLM's and bonito🐟!
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With Bonito, you can generate synthetic datasets for a wide variety of supported tasks. 
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With Bonito, you can generate synthetic datasets for a wide variety of supported tasks. 
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The Bonito model introduces a novel approach for conditional task generation, transforming unannotated text into task-specific training datasets to facilitate zero-shot adaptation of large language models on specialized data. 
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The Bonito model introduces a novel approach for conditional task generation, transforming unannotated text into task-specific training datasets to facilitate zero-shot adaptation of large language models on specialized data. 
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This methodology not only improves the adaptability of LLMs to new domains but also showcases the effectiveness of synthetic instruction tuning datasets in achieving substantial performance gains. 
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This methodology not only improves the adaptability of LLMs to new domains but also showcases the effectiveness of synthetic instruction tuning datasets in achieving substantial performance gains. 
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AutoBonito🐟: 
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AutoBonito🐟: 
https://colab.research.google.com/drive/1l9zh_VX0X4ylbzpGckCjH5yEflFsLW04?usp=sharing
https://colab.research.google.com/drive/1l9zh_VX0X4ylbzpGckCjH5yEflFsLW04?usp=sharing
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Original Repo: 
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Original Repo: 
https://github.com/BatsResearch/bonito?tab=readme-ov-file
https://github.com/BatsResearch/bonito?tab=readme-ov-file
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Paper: 
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Paper: 
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task
  Adaptation (2402.18334)
https://huggingface.co/papers/2402.18334
2402.18334
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https://huggingface.co/papers/2402.18334
Create synthetic instruction datasets using open source LLM's and bonito🐟!

With Bonito, you can generate synthetic datasets for a wide variety of supported tasks. 

The Bonito model introduces a novel approach for conditional task generation, transforming unannotated text into task-specific training datasets to facilitate zero-shot adaptation of large language models on specialized data. 

This methodology not only improves the adaptability of LLMs to new domains but also showcases the effectiveness of synthetic instruction tuning datasets in achieving substantial performance gains. 

AutoBonito🐟: https://colab.research.google.com/drive/1l9zh_VX0X4ylbzpGckCjH5yEflFsLW04?usp=sharing
Original Repo: https://github.com/BatsResearch/bonito?tab=readme-ov-file
Paper: https://huggingface.co/papers/2402.18334
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samusenps pabloce clem nihalnayak damerajee jayomb nanyy1025
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Peter Kis
NePe
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6455cc8d679315e4ef16fbec
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Tim Dolan
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