ihoneymon

[공통] 마크다운 markdown 작성법

영어지만, 조금 더 상세하게 마크다운 사용법을 안내하고 있는
"Markdown Guide (https://www.markdownguide.org/)" 를 보시는 것을 추천합니다. ^^

아, 그리고 마크다운만으로 표현이 부족하다고 느끼신다면, HTML 태그를 활용하시는 것도 좋습니다.

1. 마크다운에 관하여

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Outputs some information on CUDA-enabled devices on your computer,
including current memory usage.

It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1
from C to Python with ctypes, so it can run without compiling anything. Note
that this is a direct translation with no attempt to make the code Pythonic.

xameeramir

# Author: Zameer Ansari
# You should look at the following URL's in order to grasp a solid understanding
# of Nginx configuration files in order to fully unleash the power of Nginx.
# http://wiki.nginx.org/Pitfalls
# http://wiki.nginx.org/QuickStart
# http://wiki.nginx.org/Configuration
#
# Generally, you will want to move this file somewhere, and start with a clean
# file but keep this around for reference. Or just disable in sites-enabled.
#

russss

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)

haven-jeon

JoaoLages

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

rain-1

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.

gamingflexer

from transformers import PreTrainedTokenizerFast

fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="/home/ubuntu/LLM/module/claude-v1-tokenization.json")
text = "Hello, this is a test input."
tokens = fast_tokenizer.tokenize(text)
tokens

alielfilali01