adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

VictorTaelin

Notes on this tweet.

• The screenshots were taken on different sessions.

• The entire sessions are included on the screenshots.

• I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.

• The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.

frabert

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit

m-aciek

from alembic import config
from alembic import script
from alembic.runtime import migration
import sqlalchemy

import exceptions


engine = sqlalchemy.create_engine(DATABASE_URL)
alembic_cfg = config.Config('alembic.ini')

0polar

Jean-Baptiste “JBQ” Quéru

Public

Oct 15, 2011

Dizzying but invisible depth

You just went to the Google home page.

duhaime

from selenium import webdriver   
from selenium.webdriver.chrome.options import Options  

chrome_options = Options()  
chrome_options.add_argument("--headless")
chrome_options.binary_location = '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'

driver = webdriver.Chrome(executable_path='/usr/local/bin/chromedriver', chrome_options=chrome_options)  
driver.get("http://www.duo.com")

jnothman

import numpy as np
from sklearn import datasets
from sklearn.semi_supervised import LabelPropagation, LabelSpreading
###for n_samples in [20, 200, 2000, 20000]:
###    X, y = datasets.make_classification(n_samples=n_samples, n_classes=3, n_informative=3)
for (X, y) in [datasets.load_iris(return_X_y=True)]:
    for model in [LabelPropagation(max_iter=1000),
                  #LabelSpreading(alpha=0.01),
                  #LabelSpreading(alpha=0.1),
                  #LabelSpreading(alpha=0.3)

musically-ut

#!/bin/bash

set -e
set -u
set -o pipefail

if [ -z $1 ]
then
	echo "Usage: $0 [user@]servername"
	exit 1

musically-ut

#!/usr/bin/env bash

set -e

for N in {1..5}
do
  for M in {0.1,1,2,5,10};
  do
    # Replace ./my_prog.py with your program to run it in parallel
    ./my_prog.py -N $N -M $M > output/stdout_N${N}_M${M}.txt &

alekseykulikov

Recently CSS has got a lot of negativity. But I would like to defend it and show, that with good naming convention CSS works pretty well.

My 3 developers team has just developed React.js application with 7668 lines of CSS (and just 2 !important). During one year of development we had 0 issues with CSS. No refactoring typos, no style leaks, no performance problems, possibly, it is the most stable part of our application.

Here are main principles we use to write CSS for modern (IE11+) browsers:

• SUIT CSS naming conventions + SUIT CSS design principles;
• PostCSS + CSSNext. Future CSS syntax like variables, nesting, and autoprefixer are good enough;
• Flexbox is awesome. No need for grid framework;
• Normalize.css, base styles and variables are solid foundation for all components;