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
RLHF (Reinforcement Learning From Human Feedback): Overview + Tutorial
To fine-tune or not to fine-tune., by Michiel De Koninck
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Building a Reward Model for Your LLM Using RLHF in Python, by Fareed Khan
Reinforcement Learning from Human Feedback (RLHF)
What is Reinforcement Learning from Human Feedback (RLHF)?
Akshit Mehra - Labellerr
Data collection for LLMs - Argilla 1.14 documentation
Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU
Complete Guide On Fine-Tuning LLMs using RLHF
