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Adrian Chan's picture
4

Adrian Chan

gravity7
ยท
https://gravity7.com/
  • gravity7

AI & ML interests

GenAI for UX and CX

Recent Activity

upvoted a paper about 10 hours ago
Foundation Protocol: A Coordination Layer for Agentic Society
upvoted an article about 1 year ago
Argunauts: Open LLMs that Master Argument Analysis with Argdown
reacted to tegridydev's post with ๐Ÿ‘ over 1 year ago
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? ๐Ÿ˜ƒ 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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