content.raw: WTF is Fine-Tuning? (intro4devs) 
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content.value: WTF is Fine-Tuning? (intro4devs) 
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content.raw: Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
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content.value: Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
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content.raw: Here's a cheat sheet for devs (but open to anyone!)
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content.value: Here's a cheat sheet for devs (but open to anyone!)
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content.raw: ---
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content.value: ---
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content.raw: TL;DR
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content.value: TL;DR
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content.raw: - Full Fine-Tuning: Max performance, high resource needs, best reliability.
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content.value: - Full Fine-Tuning: Max performance, high resource needs, best reliability.
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content.raw: - PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
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content.value: - PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
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content.raw: - Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
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content.value: - Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
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content.raw: - RAFT: Best for fact-grounded models with dynamic retrieval.
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content.value: - RAFT: Best for fact-grounded models with dynamic retrieval.
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content.raw: - RLHF: Produces ethical, high-quality conversational AI, but expensive.
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content.value: - RLHF: Produces ethical, high-quality conversational AI, but expensive.
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content.raw: Choose wisely and match your approach to your task, budget, and deployment constraints.
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content.value: Choose wisely and match your approach to your task, budget, and deployment constraints.
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content.raw: I just posted the full extended article here
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content.value: I just posted the full extended article here
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content.raw: if you want to continue reading >>>
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content.value: if you want to continue reading >>>
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content.href: https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
content.raw: https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
content.type: link
rawContent: WTF is Fine-Tuning? (intro4devs) 
  
  Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃
  
  Here's a cheat sheet for devs (but open to anyone!)
  
  ---
  
  TL;DR
  
  - Full Fine-Tuning: Max performance, high resource needs, best reliability.
  - PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
  - Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
  - RAFT: Best for fact-grounded models with dynamic retrieval.
  - RLHF: Produces ethical, high-quality conversational AI, but expensive.
  
  Choose wisely and match your approach to your task, budget, and deployment constraints.
  
  I just posted the full extended article here
  if you want to continue reading >>>
  
  https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
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