WTF is Fine-Tuning? (intro4devs) 
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WTF is Fine-Tuning? (intro4devs) 
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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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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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Here's a cheat sheet for devs (but open to anyone!)
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Here's a cheat sheet for devs (but open to anyone!)
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TL;DR
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TL;DR
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- Full Fine-Tuning: Max performance, high resource needs, best reliability.
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- Full Fine-Tuning: Max performance, high resource needs, best reliability.
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- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
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- PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML.
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- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
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- Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT.
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- RAFT: Best for fact-grounded models with dynamic retrieval.
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- RAFT: Best for fact-grounded models with dynamic retrieval.
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- RLHF: Produces ethical, high-quality conversational AI, but expensive.
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- RLHF: Produces ethical, high-quality conversational AI, but expensive.
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Choose wisely and match your approach to your task, budget, and deployment constraints.
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Choose wisely and match your approach to your task, budget, and deployment constraints.
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I just posted the full extended article here
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I just posted the full extended article here
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if you want to continue reading >>>
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if you want to continue reading >>>
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https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
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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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tegridydev John6666 DaveK23 Boqua
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gravity7 josh-a AlejandroOlmedo dantezxcd