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? ๐
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 >>>
We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance.