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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
Improving your LLMs with RLHF on SageMaker
Finetuning an LLM: RLHF and alternatives (Part II), by Jose J. Martinez, MantisNLP
Exploring Reinforcement Learning with Human Feedback
Cameron R. Wolfe, Ph.D. on X: The LLM refinement process has two
7 Steps to Mastering Large Language Models (LLMs) - KDnuggets
Complete Guide On Fine-Tuning LLMs using RLHF
Supervised Fine-tuning: customizing LLMs, by Jose J. Martinez, MantisNLP
Fine-tuning LLMs 101
Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU
