LLM Fine Tuning Crash Course | LLM Fine Tuning Tutorial

Comments · 105

• CO

  @codebasics · 6 months ago · pinned

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• SE

  @selfiejones · 9 months ago

  After all these perfect explanation for cloud level fine tuning.... Bro getting bashed for 2 r&apos;s

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• PP

  @PPVAI-00 · 11 months ago

  You are a wonderful communicator. You have an incredible way of explaining concepts. Thank you very, very much.

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• BI

  @birkajay · 8 months ago

  This is wonderful. Before I started, I thought this would be just another high-level fine-tuning overview, but I was wrong. This is an excellent explanation paired with wonderful exercise. I feel so much more confident after watching this. Thank you so much

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• RO

  @RolandMyersonFinance · 5 months ago

  Good breakdown. One practical note: for proprietary financial data, PEFT/LoRA is usually the move over full fine-tuning - keeps compute costs manageable and you avoid overfitting on small domain datasets. RAG works well when your data changes frequently (e.g. real-time market data). Fine-tune for tone/format, RAG for knowledge retrieval.

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• CE

  @CertifiedAuthur · 11 months ago

  This is amazing! Thank you for this.

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• SU

  @super7ace · 10 months ago

  best video on fine tuning. thank you so much!

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• LA

  @LalithammaBestavaripeta · 4 months ago

  Thanks a lot for easy &amp; practical way of explanation !

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• SM

  @smysteriousp · 8 months ago

  Thank you sir. I have been looking for fine-tuning video for long time then I have got this one. Thank you again. 💙

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• IS

  @Isagi__000 · 11 months ago

  You are so good at explaining stuff man. Please make more AI videos in YouTube.currently i can&apos;t afford your &nbsp;boot camp.but i will try to buy when i can afford.

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• WA

  @WasimAnsaridonotbegentle · 11 months ago

  Thank you sir for bringing this much awaited video. You explanation was brilliant. I struggled through understanding why we need QLoRA and what problem it solve. Its clear now. Please launch a video im continuation of this video telling us how to quantize it and wrap the model in small package for deployment and inference.

• US

  @usmanwaris3147 · 8 months ago

  Thank you sooo much sir! your knowladge is Tremendous.Again thanks to sharing your precious knowladge with us.