| #!/usr/bin/python | |
| """ | |
| imageMe is a super simple image gallery server. | |
| Run imageme.py from the top level of an image directory to generate gallery | |
| index HTML and run a SimpleHTTPServer on the localhost. | |
| Imported as a module, use imageme.serve_dir(your_path) to do the same for any | |
| directory programmatically. When run as entry point, imageme.serve_dir('.') is | |
| what's called. |
| ### Graph Models | |
| clefourrier/graphormer-base-pcqm4mv1 | |
| ### Time Series Models | |
| huggingface/autoformer-tourism-monthly | |
| huggingface/informer-tourism-monthly | |
| huggingface/time-series-transformer-tourism-monthly | |
| ### Reinforcement Learning Models | |
| edbeeching/decision-transformer-gym-hopper-medium |
It is glad to see some LLM speed reports online such as CPU and GPU. To give a more comprehensive investigation, this document records some LLM inference measurements on V100 16GB using text-generation-webui.
We test following prompts:
How many student news papers are found at Notre Dame?
| # after running the script, use google photos their built-in timestamp based sorting function | |
| i=0 | |
| find ./ -name "*.jpg" | sort | while read filename; do | |
| i=$((i+1)) | |
| time=`date --date="-${i} seconds" '+%Y:%m:%d-%T'` | |
| #echo $time | |
| jhead -mkexif $filename |
| from selenium import webdriver | |
| from selenium.webdriver.chrome.options import Options | |
| from webdriver_manager.chrome import ChromeDriverManager | |
| from bs4 import BeautifulSoup | |
| import pandas as pd | |
| import time | |
| options = Options() | |
| options.add_argument('--headless') |
| """ | |
| start at x, each step either walk +1 or -1. If we arrive at 0 or n, then we stop. | |
| question: what's the probability to stop at 0 and n with starting position x? | |
| theoretically: P[stop at 0] = (n-x)/n, P[stop at n] = x/n | |
| simulation results in the format "x, (P[stop at 0], P[stop at n])": | |
| 2 (0.98032, 0.01968) |
| ## note, run with py27 | |
| import itertools | |
| import numpy as np | |
| from lpdec.codes import BinaryLinearBlockCode | |
| from lpdec.codes.ldpc import ArrayLDPCCode | |
| from lpdec.channels import * | |
| from lpdec.decoders.iterative import IterativeDecoder | |
| # model + meanfile, https://drive.google.com/drive/folders/1GF_hyCkw46SVxQhP0NSVB4WJraYzlhUp?usp=sharing | |
| # I0208 21:55:14.949070 18811 caffe.cpp:309] Loss: 1.65299 | |
| # I0208 21:55:14.949090 18811 caffe.cpp:321] loss3/loss3 = 1.65299 (* 1 = 1.65299 loss) | |
| # I0208 21:55:14.949101 18811 caffe.cpp:321] loss3/top-1 = 0.607879 | |
| # I0208 21:55:14.949111 18811 caffe.cpp:321] loss3/top-5 = 0.838562 | |
| # | |
| name: "eyeriss" | |
| layer { |
| <!DOCTYPE HTML> | |
| <html lang="en-US"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta http-equiv="refresh" content="0; url=https://liaocs2008.github.io/"> | |
| <script type="text/javascript"> | |
| window.location.href = "https://liaocs2008.github.io/" | |
| </script> | |
| <title>Siyu Liao</title> | |
| </head> |
| """ | |
| First Pass: | |
| ( 5 1 4 2 8 ) –> ( 1 5 4 2 8 ), Here, algorithm compares the first two elements, and swaps since 5 > 1. | |
| ( 1 5 4 2 8 ) –> ( 1 4 5 2 8 ), Swap since 5 > 4 | |
| ( 1 4 5 2 8 ) –> ( 1 4 2 5 8 ), Swap since 5 > 2 | |
| ( 1 4 2 5 8 ) –> ( 1 4 2 5 8 ), Now, since these elements are already in order (8 > 5), algorithm does not swap them. | |
| Second Pass: | |
| ( 1 4 2 5 8 ) –> ( 1 4 2 5 8 ) | |
| ( 1 4 2 5 8 ) –> ( 1 2 4 5 8 ), Swap since 4 > 2 |
