Fine-tuning LLMs can help building custom, task specific and expert models. Read this blog to know methods, steps and process to perform fine tuning using RLHF
In discussions about why ChatGPT has captured our fascination, two common themes emerge:
1. Scale: Increasing data and computational resources.
2. User Experience (UX): Transitioning from prompt-based interactions to more natural chat interfaces.
However, there's an aspect often overlooked – the remarkable technical innovation behind the success of models like ChatGPT. One particularly ingenious concept is Reinforcement Learning from Human Feedback (RLHF), which combines reinforcement learni
How to Fine Tune LLMs?
Understanding and Using Supervised Fine-Tuning (SFT) for Language
Reinforcement Learning Meets Large Language Models (LLMs): Aligning Human Preferences in LLMs, by Peyman Kor
Akshit Mehra - Labellerr
7 Steps to Mastering Large Language Models (LLMs) - KDnuggets
LLM Researcher and Scientist Roadmap: A Guide to Mastering Large
Patterns for Building LLM-based Systems & Products
Complete Guide On Fine-Tuning LLMs using RLHF
Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments - Lightning AI
Finetuning an LLM: RLHF and alternatives (Part III)
The complete guide to LLM fine-tuning - TechTalks
The complete guide to LLM fine-tuning - TechTalks