dsdanielpark

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

dsdanielpark

Maybe you've heard about this technique but you haven't completely understood it, especially the PPO part. This explanation might help.

We will focus on text-to-text language models 📝, such as GPT-3, BLOOM, and T5. Models like BERT, which are encoder-only, are not addressed.

Reinforcement Learning from Human Feedback (RLHF) has been successfully applied in ChatGPT, hence its major increase in popularity. 📈

RLHF is especially useful in two scenarios 🌟:

• You can’t create a good loss function
  • Example: how do you calculate a metric to measure if the model’s output was funny?
• You want to train with production data, but you can’t easily label your production data

dsdanielpark

People

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dsdanielpark

import cv2
import numpy as np

def deskew(im, max_skew=10):
    height, width = im.shape

    # Create a grayscale image and denoise it
    im_gs = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    im_gs = cv2.fastNlMeansDenoising(im_gs, h=3)