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nanobackpacklm's Introduction

Hao Sun

Hi! I'm an ML Research Engineer currently working on LLMs & AI Agents at a startup in Shanghai, China. I have 8 year experience on NLP & Software Engineering. I'm also an SCPD student in Artificial Intelligence at Stanford University currently researching on LMs & generative AI, and an alumni of University of California, Berkeley in Computer Science.

I co-founded and worked as CTO in startups Whimly LLC. and Beijing Youli Technology Co., Ltd focus on Travel and Social Networking web / mobile applications. I also worked as Principal Software Engineer and the AI Lead in Veeva Systems China on CRM system.

I researched and implemented an easy to hack Backpack Language Models repository in Stanford University mentored by John Hewitt, and achieved best custom project on CS224N (2023). The refined work was accepted by BlackboxNLP 2023.

I co-founded academic NGO Jungus and maintained the personality/psychological tests services with annually 5M+ testers and collaborated with top universities in China.

Works

  • Whimly LLC. (2015-2018, CTO)
  • Beijing Youli Technology Co., Ltd (2018-2020, CTO)
  • Veeva Systems China (2021-2023, Principal Software Engineer)
  • Undisclosed AI Startup (2023-, ML Research Engineer)

Research

Other Projects

Languages & Hobbies

  • Languages: Chinese (native), English (business), Japanese (N1)
  • Hobbies: anime, games, travel, musical, global history

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nanobackpacklm's Issues

Might need more parameters in sense network

In my reference implementation of Backpacks, our last feed-forward layer of the sense network has layer sizes:

  • In: d
  • Intermediate: 4*d
  • Out: k*d

Intuitively, having the larger intermediate layer allows for less linear dependence between the k*d output vectors. We found this to be important at our largest model size (170M).

In this implementation, there is no such intermediate increase; the implementation has:

  • In: d
  • Intermediate: d
  • Out: k*d

self.ff_2 = BackpackFF(config.n_embd, config.n_embd * config.n_sense_vector, config.dropout, config.bias)

This does use quite a few more parameters, I realize -- but this is just to keep in mind, since I think once you have a ton of sense vectors it will become increasingly important to devote more parameters to them. (One could even imagine just learning all the sense vectors from scratch, not parameterized by an MLP, since they're sparsely activated...)

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