this post was submitted on 13 Nov 2023
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Machine Learning

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Hello folks,

I have an interview call later this week which the work is regarding implementing generative AI within the companies workflow. Using LLMs with finetuning/in-context learning using system logs etc kind of stuff.

I have studied machine learning, worked for few years now as well. Have good understanding of those stuff but never tried fine tuning hands-on. I'm worked majority into computer-vision applications but think that I lagged a bit on the LLM side.

Any suggestions, recommeded papers, courses, videos I could go through?

Thanks!

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[–] RonLazer@alien.top 1 points 1 year ago (1 children)

Not an exhaustive list, but here's a few suggestions:

  1. Get really familiar with embedding/semantic-search/RAG.
  2. Fine-tune a LLaMA-2 7B model using QLoRA on an A10 EC2 instance (or whatever compute you have) to do something like document classification or sentiment analysis.
  3. Watch this video: https://www.youtube.com/watch?v=yj-wSRJwrrc
  4. Read this paper: https://arxiv.org/pdf/2311.04235.pdf
[–] ade17_in@alien.top 1 points 1 year ago

Thanks alot. Surely helpful

[–] saintshing@alien.top 1 points 1 year ago (1 children)
[–] AnotherBotIGuess@alien.top 1 points 1 year ago

An actual saint

[–] cipher982@alien.top 1 points 1 year ago (1 children)

I put together an overview of fine-tuning recently, focusing on some more practical techniques when you really get into the process. https://david010.medium.com/fine-tuning-llms-practical-techniques-and-helpful-tips-3a169cc62cca

[–] ade17_in@alien.top 1 points 1 year ago

Thanks for this. Super useful

[–] PositiveAlfalfa3849@alien.top 1 points 1 year ago

what level of education are you? PhD?

[–] tensorgym@alien.top 1 points 11 months ago (1 children)

Take a look at TensorGym, which offers practical hands-on ML coding exercises (disclosure: I am building TensorGym)
Good luck with the interview!

[–] ade17_in@alien.top 1 points 11 months ago

Thanks! Bombed that interview but will try it for my next one!