threepi 4 days ago | next |

Author here. Happy to see this posted here. This is actually a series of blog posts:

1. Exploring LoRA — Part 1: The Idea Behind Parameter Efficient Fine-Tuning and LoRA: https://medium.com/inspiredbrilliance/exploring-lora-part-1-...

2. Exploring LoRA - Part 2: Analyzing LoRA through its Implementation on an MLP: https://medium.com/inspiredbrilliance/exploring-lora-part-2-...

3. Intrinsic Dimension Part 1: How Learning in Large Models Is Driven by a Few Parameters and Its Impact on Fine-Tuning https://medium.com/inspiredbrilliance/intrinsic-dimension-pa...

4. Intrinsic Dimension Part 2: Measuring the True Complexity of a Model via Random Subspace Training https://medium.com/inspiredbrilliance/intrinsic-dimension-pa...

Hope you enjoy reading the other posts too. Merry Christmas and Happy Holidays!

jwildeboer 3 days ago | prev | next |

(Not to be confused with LoRa, (short for long range) which is a spread spectrum modulation technique derived from chirp spread spectrum (CSS) technology, powering technologies like LoRaWAN and Meshtastic)

SeasonalEnnui 3 days ago | root | parent | next |

This gets me every time. I expect to see something interesting and it turns to be the other one. One is a fantastic thing and the other is mediocre, pick which way round at your discretion!

pavlov 3 days ago | root | parent | prev |

What exactly is the confusion? Does “parameter efficient fine-tuning” mean anything in context of the other Lora? If not, then it’s probably obvious which one this is about.

mrgaro 3 days ago | root | parent |

Actually it does: Lora the radio protocol has parameters to tune. Usually both sender and receiver needs to match these, so I read this like a method how these could be automatically tuned based on the distance and radio environment.