Tiri-J - v1.2
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Josh
josh-a
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AI & ML interests
I build things, like advanced Localization models.
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tegridydev's
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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
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