RenzeLou/Muffin
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
This project helps developers curate high-quality datasets for training large language models (LLMs) to follow instructions more accurately. It takes raw text inputs and, using existing LLMs, generates diverse instructions or matches them with relevant tasks. The output is a structured dataset containing inputs paired with multiple, well-suited instructions, ideal for improving LLM performance on complex tasks.
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Use this if you are a machine learning engineer or researcher focused on developing and improving instruction-following capabilities in large language models.
Not ideal if you are a general user looking for an out-of-the-box application to solve a specific business problem, as this is a developer tool for dataset creation.
Stars
16
Forks
3
Language
Python
License
MIT
Category
Last pushed
Oct 31, 2024
Commits (30d)
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